Abstract:
Datacenter clusters often employ live virtual machine (VM) migration to efficiently utilize cluster-wide resources. Gang migration refers to the simultaneous live migration of multiple VMs from one set of physical machines to another in response to events such as load spikes and imminent failures. Gang migration generates a large volume of network traffic and can overload the core network links and switches in a data center. The present technology reduces the network overhead of gang migration using global deduplication (GMGD). GMGD identifies and eliminates the retransmission of duplicate memory pages among VMs running on multiple physical machines in the cluster. A prototype GMGD reduces the network traffic on core links by up to 51% and the total migration time of VMs by up to 39% when compared to the default migration technique in QEMU/KVM, with reduced adverse performance impact on network-bound applications.

Description:
STATEMENT OF GOVERNMENT SUPPORT 
     This invention was made with government support under award 0845832 awarded by the National Science Foundation. The government has certain rights in this invention. 
    
    
     BACKGROUND OF THE INVENTION 
     Live migration of virtual machines (VMs) is a critical activity in the operation of modern data centers. Live migration involves the transfer of multiple Gigabytes of memory within a short duration (assuming that network attached storage is used, which does not require migration) and can consequently consume significant amounts of network and CPU resources. 
     An administrator may need to simultaneously migrate multiple VMs to perform resource re-allocation to handle peak workloads, imminent failures, cluster maintenance, or powering down an entire rack to save energy, Simultaneous live migration of multiple VMs is referred to as gang migration[8]. Gang migration is a network intensive activity that can cause an adverse cluster-wide impact by overloading the core links and switches of the datacenter network. Gang migration can also affect the performance at the network edges where the migration traffic competes with the bandwidth requirements of applications within the VMs. Hence it is important to minimize the adverse performance impact of gang migration by reducing the total amount of data transmitted due to VM migration. Reducing the VM migration traffic can also lead to a reduction in the total time required to migrate multiple VMs. 
     Process migration has also been extensively researched. Numerous cluster job schedulers exist, as well as virtual machine management systems, such as VMWare&#39;s DRS, XenEnterprise, Usher, Virtual Machine Management Pack, and CoD that let administrators control jobs/VM placement based on cluster load or specific policies such as affinity or anti-affinity rules. 
     [27] optimizes the live migration of a single VM over wide-area network through a variant of stop-and-copy approach which reduces the number of memory copying iterations. [30] and [27] further use page-level deduplication along with the transfer of differences between dirtied, and original pages, eliminating the need to retransmit the entire dirtied page. [16] uses an adaptive page compression technique to optimize the live migration of a single VM. Post-copy [13] transfers every page to the destination only once, as opposed to the iterative pre-copy[20], [5], which transfers dirtied pages multiple times. [14] employs low-overhead RDMA over Infiniband to speed up the transfer of a single VM. [21] excludes the memory pages of processes communicating over the network from being transferred during the initial rounds of migration, thus limiting the total migration time. [29] shows that certain benchmarks used in high performance computing are likely to have large amounts of content sharing. The work focuses mainly on the opportunity and feasibility of exploiting content sharing, but does not provide an implementation of an actual migration mechanism using this observation, nor does it evaluate the migration time or network traffic reduction. Shrinker[22] migrates virtual clusters over high-delay links of WAN. It uses an online hashing mechanism in which hash computation for identifying duplicate pages (a CPU-intensive operation) is performed during the migration. 
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     SUMMARY OF THE INVENTION 
     The present technology provides, for example, live gang migration of multiple VMs that run on multiple physical machines, which may be in a cluster or separated by a local area network or wide area network. A cluster is assumed to have a high-bandwidth low-delay interconnect such has Gigabit Ethernet[10], 10 GigE[9], or Infiniband[15]. Wide Area Networks tend to have lower throughput, lower communications reliability, and higher latency than communications within a cluster. One approach to reducing the network traffic due to gang migration uses the following observation. VMs within a cluster often have similar memory content, given that they may execute the same operating system, libraries, and applications. Hence, a significant number of their memory pages may be identical[25]. Similarly, VMs communicating over less constrained networks may also share memory content. 
     One can reduce the network overhead of gang migration using deduplication, i.e. by avoiding the transmission of duplicate copies of identical pages. One approach is called gang migration using global deduplication (GMGD), which performs deduplication during the migration of VMs that run on different physical machines. In contrast, gang migration using local deduplication (GMLD) refers to deduplicating the migration of VMs running within a single host[8]. 
     Various aspects which may be used include: A technique to identify and track identical memory content across VMs running on different physical machines in a cluster, including non-migrating VMs running on the target machines; and a technique to deduplicate this identical memory content during the simultaneous live migration of multiple VMs, while keeping the coordination overhead low. 
     For example, an implementation of GMGD may be provided on the QEMU/KVM[18] platform. A quantitative evaluation of GMGD on a 30 node cluster test bed having 10 GigE core links and 1 Gbps edge links was performed, comparing GMGD against two techniques—the QEMU/KVM&#39;s default live migration technique, called online compression (OC), and GMLD. 
     Prior efforts to reduce the data transmitted during VM migration have focused on live migration of a single VM[5], [20], [13], [16], live migration of multiple VMs running on the same physical machine (GMLD) [8], live migration of a virtual cluster across a wide-area network (WAN)[22], or non-live migration of multiple VM images across a WAN[17]. Compared to GMLD, GMGD faces the additional challenge of ensuring that the cost of global deduplication does not exceed the benefit of network traffic reduction during the live migration. The deduplication cost may be calculated, inferred or presumed. In contrast to migration over a WAN, which has high-bandwidth high-delay links, migration within a datacenter LAN has high-bandwidth low-delay links. This difference is important because hash computations, which are used to identify and deduplicate identical memory pages, are CPU-intensive operations. When migrating over a LAN, hash computations become a serious bottleneck if performed on line during migration, whereas over a WAN, the large round-trip latency can mask the online hash computation overhead. 
     First, a distributed duplicate tracking phase identifies and tracks identical memory content across VMs running on same/different physical machines in a cluster, inducting non-migrating VMs running on the target machines. The key challenge here is a distributed indexing mechanism that computes content hashes on VMs&#39; memory content on different machines and allows individual nodes to efficiently query and locate identical pages. Two options are a distributed hash table or a centralized indexing server, both of which have their relative merits and drawbacks. The former prevents a single point of bottleneck/failure, whereas the latter simplifies the overall indexing and lookup operation during runtime. 
     Secondly, a distributed deduplication phase, during the migration phase, avoids the need for re-transmission of identical memory content, that was identified in the first step, during the simultaneous live migration of multiple VMs. The goal here is to reduce the network traffic generated by migration of multiple VMs by eliminating the retransmission of identical pages from different VMs. Note that the deduplication operation would itself introduce control traffic to identify which identical pages have already been transferred from the source to the target racks. This control traffic overhead is minimized, in terms of both additional bandwidth and latency introduced due to synchronization. Deduplication has been used to reduce the memory footprint of VMs in[3],[25],[19],[1],[28] and[11]. These techniques use deduplication to reduce memory consumption either within a single VM or between multiple co-located VMs. In contrast, the present technology uses cluster-wide deduplication across multiple physical machines to reduce the network traffic overhead when simultaneously migrating multiple VMs. Non-live migration of a single VM can be speeded up by using content hashing to detect blocks within the VM image that are already present at the destination[23]. VMFlock[17] speeds up the non-live migration of a group of VM images over a high-bandwidth high-delay wide-area network by deduplicating blocks across the VM images. In contrast, one embodiment of the present technology focuses on reducing the network performance impact of the live and simultaneous migration of the memories of multiple VMs within a high-bandwidth low-delay datacenter network. The technology can of course be extended outside of these presumptions. 
     In the context of live migration of multiple VMs, GMLD[8] deduplicates the transmission of identical memory content among VMs co-located within a single host. It also exploits sub-page level &amp;duplication, page similarity, and delta difference for dirtied pages, all of which can be integrated in GMGD. 
     The large round-trip latency of WAN links masks the high hash computation overhead during migration, and therefore makes online hashing feasible. Over low-delay links, e.g., Gigabit Ethernet LAN, offline hashing appears preferable. 
     Gang migration with global deduplication (GMGD) provides a solution to reduce the network load resulting from the simultaneous live migration of multiple VMs within a datacenter that has high-bandwidth low-latency interconnect, and has implications for other environments. The technology employs cluster-wide deduplication to identify, track, and avoid the retransmission of pages that have identical content. Evaluations of a GMGD prototype on a 30 node cluster show that GMGD reduces the amount of data transferred over the core links during migration by up to 51% and the total migration time by up to 39% compared to online compression. A similar technology may be useful for sub-page-level deduplication, which advantageously would reduce the amount of data that needs to be transferred. Ethernet multicast may also be used to reduce the amount of data that needs to be transmitted. 
     Although we describe GMGD in the context of its use within a single datacenter for clarity, GMGD can also be used for migration of multiple VMs between multiple datacenters across a wide-area network (WAN). The basic operation of GMGD over a WAN remains the same. 
     Compared to existing approaches that use online hashing/compression, GMGD uses an offline duplicate tracking phase. This would in fact eliminate the computational overhead of hash computation during the migration of multiple VMs over the WAN and improve the overall performance applications that execute within the VMs. 
     Furthermore, as WAN link latencies reduce further, the cost of performing online hash computation (i.e. during migration) for large number of VMs would continue to increase. This would make GMGD more attractive due to its use of offline duplicate tracking phase. 
     It is therefore an object to provide a system and method for gang migration with global deduplication, comprising: providing a datacenter comprising a plurality of virtual machines in a cluster defined by a set of information residing in a first storage medium, the cluster communicating through at least one data communication network; performing cluster-wide deduplication of the plurality of virtual machines to identify redundant memory pages of the first storage medium representing the respective virtual machines that have corresponding content;
         initiating a simultaneous live migration of the plurality of virtual machines in the cluster, by communicating information sufficient to reconstitute the plurality of virtual machines in a cluster defined by the set of information residing in a second storage medium, through the at least one data communication network; based on the identification of the redundant memory pages having corresponding content, selectively communicating information representing the unique memory pages of the first storage medium through the at least one communication network to the second storage medium, substantially without communicating all of the memory pages of the first storage medium; and subsequent to communication through the at least one communication network, duplicating the redundant memory pages of the first storage medium in the second storage medium selectively dependent on the identified redundant memory pages, to reconstitute the plurality of virtual machines in the second storage medium.       

     It is also an object to provide a system for gang migration with global deduplication, in a datacenter comprising a plurality of virtual machines in a cluster defined by a set of information residing in a first storage medium, the cluster communicating through at least one data communication network, comprising: at least one processor configured to perform cluster-wide deduplication of the plurality of virtual machines to identify redundant memory pages of the first storage medium representing the respective virtual machines that have corresponding content; at least one communication link configured to communicate a simultaneous live migration of the plurality of virtual machines in the cluster, by communicating information sufficient to reconstitute the plurality of virtual machines in a cluster defined by the set of information residing in a second storage medium, through the at least one data communication network: the at least one processor being further configured, based on the identification of the redundant memory pages having corresponding content, to selectively communicate information representing the unique memory pages of the first storage medium through the at least one communication network to the second storage medium, substantially without communicating all of the memory pages of the first storage medium, and subsequently to communicate through the at least one communication network, duplicating the redundant memory pages of the first storage medium in the second storage medium selectively dependent on the identified redundant memory pages, to reconstitute the plurality of virtual machines in the second storage medium. 
     It is a still further object to provide a method for migration of virtual machines with global &amp;duplication, comprising: providing a plurality of virtual machines at a local facility, defined by a set of stored information comprising redundant portions, the network being interconnected with a wide area network; identifying at least a subset of the redundant portions of the stored information; initiating a simultaneous live migration of the plurality of virtual machines by communicating through the wide area network to the remote location data sufficient to reconstitute the set of stored information comprising the identification of the subset of the elements of the redundant portions and the set of stored information less redundant ones of the subset of the redundant portions of the stored information; receiving at a remote location the data sufficient to reconstitute the set of stored information; duplicating the subset of the redundant portions of the stored information to reconstitute the set of stored information defining the plurality of virtual machines; and transferring an active status to the reconstituted plurality of virtual machines at the remote location 
     The identification of redundant portions or pages of memory is advantageously performed using a hash table, which can be supplemented with a dirty or delta table, such that the hash values need not all be recomputed in real time. A hash value of memory portion which remains unchanged can be computed once, and so long as it remains unchanged, the hash value maintained. Hash values of pages or portions which change dynamically can be recomputed as necessary. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an illustration of GMGD; 
         FIG. 2  shows deduplication of identical pages during migration; 
         FIG. 3  shows the layout of the testbed used for evaluation; 
         FIG. 4  illustrates network traffic on core links when migrating idle VMs; 
         FIG. 5  illustrates network traffic on core links when migrating busy VMs; 
         FIG. 6  shows a downtime comparison; 
         FIG. 7  shows the total migration time with background traffic; 
         FIG. 8  shows background traffic performance with gang migration; and 
         FIG. 9  illustrates the proposed scatter-gather based live VM migration. 
         FIG. 10  shows a block diagram of a known computer network topology. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Architecture 
     The high-level architecture of GMGD is shown with respect to  FIG. 1 . 
     For simplicity of exposition, we first describe how GMGD operates when VMs are live migrated from one rack of machines to another rack, followed by a description of its operation in the general case. For each VM being migrated, the target physical machine is provided as an input to GMGD. Target mapping of VMs could be provided by another VM placement algorithm that maximizes some optimization criteria such as reducing inter-VM communication overhead[26] or maximizing the memory sharing potential[28]. GMGD does not address the VM placement problem nor does it make any assumptions about the lack or presence of inter-VM dependencies. 
     As shown in  FIG. 1 , a typical cluster consists of multiple racks of physical machines. Page P is identical among all four VMs at the source rack. VM 1  and VM 3  are being migrated to target rack  1 . VM 2  and VM 4  are being migrated to target rack  2 . One copy of P is sent to host  5  which forwards P to host  6  in target rack  1 . Another copy of P is sent to host  8  which forwards P to host  9  in target rack  2 . Thus identical pages headed for the same target rack are sent only once per target rack over the core network, reducing network traffic overhead. 
     Machines within a rack are connected to a top-of-the-rack (TOR) switch. TOR switches are connected to one or more core switches using high-bandwidth links (typically 10 Gbps or higher). GMGD does not preclude the use of other layouts where the core network could become overloaded. Migrating VMs from one rack to another increases the network traffic overhead on the core links. To reduce this overhead, GMGD employs a cluster-wide deduplication mechanism to identify and track identical pages across VMs running on different machines. As illustrated in  FIG. 1 , GMGD identifies the identical pages from VMs that are being migrated to the same target rack (or more generally, the same facility) and transfers only one copy of each identical page to the target rack. At the target rack, the first machine to receive the identical page transfers the page to other machines in the rack that also require the page. This prevents duplicate transfers of an identical page over the core network to the same target rack. GMGD can work with any live VM migration technique, such as pre-copy[5] or post-copy[13]. In the prototype system described below, GMGD was implemented within the default pre-copy mechanism in QEMU/KVM. GMGD has two phases, namely duplicate tracking and live migration. 
     Physical machines in enterprise clusters often have multiple network interface cards (NICs) to increase the network bandwidth available to each node. The availability of multiple NICs may be exploited to reduce the total migration time of live gang migration. The basic idea is that memory pages from each VM can be potentially be scattered during migration to multiple nodes at the target machine&#39;s rack. The scattered pages could then be gathered by the target machine through parallel transfers over multiple NICs. At the first look, this scatter-gather approach seems to introduce an additional hop in the page transfer between the source and the target. However, when scatter-gather operation is combined with distributed deduplication across multiple VMs, the performance advantages of the approach becomes apparent. In essence, pages with identical content on different VMs are scattered to the same machine on the target rack. Only the first copy of the identical page needs to be transferred, whereas subsequent pages are communicated via their unique identifiers (which includes VM&#39;s ID, target machine&#39;s ID, page offset and content hash). 
     A. Duplicate Tracking Phase 
     The Duplicate Tracking Phase is carried out during normal execution of VMs at the source machines, before the migration begins. Its purpose is to identify all duplicate memory content (e.g., at the page-level) across all VMs residing on different machines. Content hashing is used to detect identical pages. The pages having the same content yield the same hash value. When the hashing is performed using a standard 160-bit SHA1 hash[12], the probability of collision is less than the probability of a memory error, or an error in a TCP connection[4]. Of course, different hashing or memory page identification technologies might be used. For example, in some environments, static content is mapped to memory locations, in which case, the static content need only be identified, such as with a content vector. In other cases, especially where local processing capacity is available, a memory page which differs by a small amount from a reference page may be coded by its differences. Of course, other technologies which inferentially define the content of the memory can be used. 
     In each machine, a per-node controller process coordinates the tracking of identical pages among all VMs in the machine. The per-node controller instructs a user-level QEMU/KVM process associated with each VM to scan the VM&#39;s memory image, perform content based hashing and record identical pages. Since each VM is constantly executing, some of the identical pages may be modified (dirtied) by the VM, either during the hashing, or after its completion. To identify these dirtied pages, the controller uses the dirty logging mode of QEMU/KVM. In this mode, all VM pages are marked as read-only in the shadow page table maintained by the hypervisor. The first write attempt to any read-only page results in a trap into the hypervisor which marks the faulted page as dirty in its dirty bitmap and allows the write access to proceed. The QEMU/KVM process uses a hypercall to extract the dirty bitmap from KVM to identify the modified pages. 
     The per-rack deduplication servers maintain a hash table, which is populated by carrying out a rack-wide content hashing of the 160-bit hash values pre--computed by per-node controllers. Each hash is also associated with a list of hosts in the rack containing the corresponding pages. Before migration, all deduplication servers exchange the hash values and host list with other deduplication servers. 
     In some cases, data in memory is maintained even though the data structures corresponding to those memory pages are no longer in use. In order to avoid need for migration of such data, a table may be maintained of “in use” or “available” memory pages, and the migration limited to the live data structures or program code. In many cases, operating system resources already maintain such table(s), and therefore these need not be independently created or maintained. 
     B. Migration Phase 
     In this phase, all VMs are migrated in parallel to their destination machines. The pre-computed hashing information is used to perform the deduplication of the transferred pages at both the host and the rack levels. QEMU/KVM queries the deduplication server for its rack to acquire the status of each page. If the page has not been transferred already by another VM, then its status is changed to sent and it is transferred to the target QEMU/KVM. For subsequent instances of the same page from any other VM migrating to the same rack, QEMU/KVM transfers the page identifier. Deduplication servers also periodically exchange the information about the pages marked as sent, which allows the VMs in one rack to avoid retransmission of the pages that are already sent by the VMs from another rack. 
     C. Target-side VM Deduplication 
     The racks used as targets for VM migration are often not empty. They may host VMs containing pages that are identical to the ones being migrated into the rack. Instead of transferring such pages from the source racks via core links, they are forwarded within the target rack from the hosts running the VMs to the hosts receiving the migrating VMs. The deduplication server at the target rack monitors the pages within hosted VMs and synchronizes this information with other deduplication servers. Per-node controllers perform this forwarding of identical pages among hosts in the target rack. 
     D. Scatter-Gather VM Deduplication 
       FIG. 9  shows the potential architecture of the system for a two-rack scenario, for simplicity of exposition. The system is easily generalized for a larger multi-rack scenario. One or more VMs are migrated from source machine(s) in one rack to target machine(s) in another rack. 
     Machines in each rack together export a virtual memory device, which is essentially a logical device that aggregates the free memory space available on each machine in the rack. Within each node, the virtual memory device is exported via the block device interface, which is normally used to perform I/O operations. Such a virtualized memory device can be created using the MemX system[31], [32], [33]. See, osnet.cs.binghamton.edu/projects/memx.html, expressly incorporated herein by reference. 
     At the source node, memory pages of the VMs being migrated are written to the virtual memory device, which transparently scatters the pages over the network to machines in Rack  2  and keeps track of their location using a distributed hash table. These pages are also deduplicated against identical pages belonging to other VMs. The target node then reads the pages from the virtual memory device, which transparently gathers pages from other nodes on Rack  2 . 
     Note that the scatter-gather approach can be used with both pre-copy and post-copy migration mechanisms. With pre-copy, the scatter and gather phases overlap with the iterative copy phase, enabling the latter to complete quickly, so that the source can initiate downtime earlier than it would have through traditional pre-copy. With traditional pre-copy, the source node may take a long time to initiate downtime depending upon whether the workload is read-intensive or write-intensive. With post-copy, the scatter operation allows active-push phase to quickly eliminate residual state from the source node, and the gather phase quickly transfers the memory content to the intended target host. 
     The scatter and gather operation can use multiple NICs at the source and target machines to perform parallel transfer of memory pages. In addition, with the availability of multi-core machines, multiple parallel threads at each node can carry out parallel reception and processing of the VM&#39;s memory pages. These two factors, combined with cluster-wide deduplication, will enable significant speedups in simultaneous migration of multiple VMs in enterprise settings. 
     EXAMPLE 
     A prototype of GMGD was implemented in the QEMU/KVM virtualization environment. The implementation is completely transparent to the users of the VMs. With QEMU/KVM, each VM is spawned as a process on a host machine. A part of the virtual address space of the QEMU/KVM process is exported to the VM as its physical memory. 
     A. Per-node Controllers 
     Per-node controllers are responsible for managing the deduplication of outgoing and incoming VMs. The controller component managing the outgoing VMs is called the source side and component managing the incoming VMs is called the target side. The controller sets up a shared memory region that is accessible only by other QEMU/KVM processes. The shared memory contains a hash table which is used for tracking identical pages. Note that the shared memory poses no security vulnerabilities because it is outside the physical memory region of the VM in the QEMU/KVM process&#39; address space and is not accessible by the VM itself. 
     The source side of the per-node controller coordinates the local deduplication of memory among co-located VMs. Each QEMU/KVM process scans its VM&#39;s memory and calculates a 160-bit SHA1 hash for each page. These hash values are stored in the hash table, where they are compared against each other. A match of two hash values indicates the existence of two identical pages. Scanning is performed by a low priority thread to minimize interference with the VMs&#39; execution. It is noted that the hash table may be used for other purposes, and therefore can be a shared resource with other facilities. 
     The target side of the per-node controller receives incoming identical pages from other controllers in the rack. It also forwards the identical pages received on behalf of other machines in the rack to their respective controllers. Upon reception of an identical page, the controller copies the page into the shared memory region, so that it becomes available to incoming VMs. 
     B. Deduplication Server 
     Deduplication servers are to per-node controllers what per-node controllers are to VMs. Each rack contains a deduplication server that tracks the status of identical pages among VMs that are migrating to the same target rack and the VMs already at the target rack. Deduplication servers maintain a content hash table to store this information. Upon reception of a 160-bit hash value from the controllers, the last 32-bits of the 160-bit hash are used to find a bucket in the hash table. In the bucket, the 160-bit hash entry is compared against the other entries present. If no matching entry is found, a new entry is created. 
     Each deduplication server can currently process up to 200,000 queries per second over a 1 Gbps link. This rate can potentially handle simultaneous VM migrations from up to 180 physical hosts. For context, common 19-inch racks can hold 44 servers of 1U (1 rack unit) height[24]. A certain level of scalability is built into the deduplication server by using multiple threads for query processing, fine-grained reader/writer locks, and batching queries from VMs to reduce the frequency of communication with the deduplication server. 
     Finally, the deduplication server does not need to be a separate server per rack. It can potentially run as a background process within one of the machines in the rack that also runs VMs provided that a few spare CPU cores are available for processing during migration. 
     C. Operations at the Source Machine 
     Upon initiating simultaneous migration of VMs, the controllers instruct individual QEMU/KVM processes to begin the migration. From this point onward, the QEMU/KVM processes communicate directly with the deduplication servers, without any involvement from the controllers. After commencing the migration, each QEMU/KVM process starts transmitting every page of its respective VM. For each page it checks in the local hash table whether the page has already been transferred. Each migration process periodically queries its deduplication server for the status of next few pages it is about to transfer. The responses from the deduplication server are stored into the hash table, in order to be accessible to the other co-located VMs. If the QEMU/KVM process discovers that a page has not been transferred, then it transmits the entire page to its peer QEMU/KVM process at the target machine along with its unique identifier. QEMU/KVM at the source also retrieves from the deduplication server a list of other machines in the target rack that need an identical page. This list is also sent to the target machine&#39;s controller, which then retrieves the page and sends it to the machines in the list. Upon transfer the page is marked as sent in the source controller&#39;s hash table. The QEMU/KVM process periodically updates its deduplication server with the status of the sent pages. The deduplication server also periodically updates other deduplication servers with a list of identical pages marked as sent. Dirty pages and unique pages that have no match with other VMs are transferred in their entirety to the destination. 
       FIG. 2  shows the message exchange sequence between the deduplication servers and QEMU/KVM processes for an inter-host deduplication of page P. 
     D. Operations at the Target Machine 
     On the target machine each QEMU/KVM process allocates a memory region for its respective VM where incoming pages are copied. Upon reception of an identical page, the target QEMU/KVM process copies it into the VM&#39;s memory and inserts it into the target hash table according to its identifier. If only an identifier is received, a page corresponding to the identifier is retrieved from the target hash table, and copied into the VM&#39;s memory. Unique and dirty pages are directly copied into the VMs&#39; memory space. 
     E. Remote Pages 
     Remote pages are deduplicated pages that were transferred by hosts other than the source host. Identifiers of such pages are accompanied by a remote flag. Such pages become available to the waiting hosts in the target rack only after the carrying host forwards them. Therefore, instead of searching for such remote pages in the target hash table immediately upon reception of an identifier, the identifier and the address of the page are inserted into a per-host waiting list. A per QEMU/KVM process thread, called a remote thread, periodically traverses the list, and checks for each entry if the page corresponding to the identifier has been added into the target shared memory. The received pages are copied into the memory of the respective VMs after removing the entry from the list. Upon reception of a more recent dirtied copy of the page whose entry happens to be on the waiting list, the corresponding entry is removed from the list to prevent the thread from over-writing the page with its stale copy. The identical pages already present at the target rack before the migration are also treated as the remote pages. The per-node controllers in the target rack forward such pages to the listed target hosts. This avoids their transmission over the core network links from the source racks. However, pages dirtied by VMs running in the target rack are not forwarded to other hosts and they are requested by the corresponding hosts from their respective source hosts. 
     F. Downtime Synchronization 
     Initiating a VM&#39;s downtime before completing target-to-target transfers can increase its downtime duration. However, in the default QEMU/KVM migration technique, downtime is started at the source&#39;s discretion and the decision is made solely on the basis of the number of pages remaining to be transferred and the perceived link bandwidth at the source. Therefore, to avoid the overlap between the downtime and target-to-target transfers, a synchronization mechanism is implemented between the source and the target QEMU/KVM processes. The source QEMU/KVM process is prevented from starting the VM downtime and keep it in the live pre-copy iteration mode until all of its pages have been retrieved at the target and copied into memory. Once all remote pages are in place, the source is instructed by the target to initiate the downtime. This allows VMs to minimize their downtime, as only the remaining dirty pages at the source are transferred during the downtime. 
     G. Desynchronizing Page Transfers 
     An optimization was implemented to improve the efficiency of deduplication. There is a small time lag between the transfer of an identical page by a VM and the status of the page being reflected at the deduplication server. This lag can result in duplicate transfer of some identical pages if two largely identical VMs start migration at the same time and transfer their respective memory pages in the same order of page offsets. To reduce such duplicate transfers, each VM transfers pages in different order depending upon their assigned VM number, so as to break any synchronization with other VMs. This reduces the likelihood that identical pages from different VMs may be transferred around the same time. 
     Evaluation 
     GMGD was evaluated in a  30 -node cluster testbed having high-bandwidth low-latency Gigabit Ethernet. Each physical host has two Quad core 2 GHz CPUs, 16 GB of memory, and 1 Gbps network card.  FIG. 3  shows the layout of the cluster testbed consisting of three racks, each connected to a different top of rack (TOR) Ethernet switch. The TOR switches are connected to each other by a 10 GigE optical link, which acts as the core link. Although we had only the 30-node three-rack topology available for evaluation, GMGD can be used on larger topologies. Live migration of all VMs is initiated simultaneously and memory pages from the source hosts traverse the 10 GigE optical link between the switches to reach the target hosts. For most of the experiments, each machine hosts four VMs and each VM has 2 virtual CPUs (VCPUs) and 1 GB memory. We compare GMGD against the following VM migration techniques. 
     (1) Online Compression (OC): 
     This is the default VM migration technique used by QEMU/KVM. Before transmission, it compresses pages that are filled with uniform content (primarily pages filled with zeros) by representing the entire page with just one byte. At the target, such pages are reconstructed by filling an entire page with the same byte. Other pages are transmitted in their entirety to the destination. 
     (2) Gang Migration With Local Deduplication (GMLD): 
     This technique uses content hashing to deduplicate the pages across VMs co-located on the same host[8]. Only one copy of identical pages is transferred from the source host. 
     In initial implementations of GMGD prototype, the use of online hashing was considered, in which hash computation and deduplication are performed during migration (as opposed to before migration). Hash computation is a CPU-intensive operation. In the evaluations, it was found that the online hashing variant performed very poorly, in terms of total migration time, on high-bandwidth low-delay Gigabit Ethernet. For example, online hashing takes 7.3 seconds to migrate a 1 GB VM and 18.9 seconds to migrate a 4 GB VM, whereas offline hashing takes only 3.5 seconds and 4.5 seconds respectively. CPU-heavy online hash computation became a serious performance bottleneck and, in fact, yielded worse total migration times than even the simple OC technique described above. Given that the total migration time of online hashing variant is considerably worse than offline hashing, but the savings in network traffic are just comparable, the results for online hashing are omitted in the reports of experiments below. 
     A. Network Load Reduction 
     1) Idle VMs: Here an equal number of VMs are migrated from each of the two source racks, i.e., for 12×4 configuration, 4 VMs are migrated from each of the 6 hosts on each source rack.  FIG. 4  shows the amount of data transferred over the core links for the three VM migration schemes with an increasing number of hosts, each host running four 1 GB idle VMs. Since OC only optimizes the transfer of uniform pages, a set that mainly consists of zero pages, it transfers the highest amount of data. GMLD also deduplicates zero pages in addition to the identical pages across the co-located VMs. As a result, it sends less data than OC. GMGD transfers the lowest amounts of data. For 12 hosts, GMGD shows more than 51%, and 19% decrease in the data transferred through the core links over OC and GMLD respectively. 
     2) Busy VMs: To evaluate the effect of busy VMs on the amount of data transferred during their migration, Dbench[6], a filesystem benchmark, was run inside VMs. Dbench performs file I/O on a network attached storage. It provides an adversarial workload for GMGD because it uses the network interface for communication and DRAM as a buffer. Dbench was modified to write random data, hence its memory footprint consisted of unique pages that cannot be deduplicated. Also the execution of Dbench was initiated after the deduplication phase of GMGD to ensure that the memory consumed by Dbench was not deduplicated. The VMs are migrated while execution of Dbench is in progress.  FIG. 5  shows that GMGD yields a 48% reduction in the amount of data transferred over OC and 18% reduction over GMLD. 
     B. Total Migration Time 
     1) Idle VMs: To measure the total migration time of different migration techniques, the end-to-end (E2E) total migration time is measured, i.e. the time taken from the start of the migration of the first VM to the end of the migration of the last VM. Cluster administrators are concerned with E2E total migration time of groups of VMs since it measures the time for which the migration traffic occupies the core links. The idle VM section of Table I shows the total migration time for each migration technique with an increasing number of hosts containing idle VMs. Note that even with the maximum number of hosts (i.e. 12 with 6 from each source rack), the core optical link remains unsaturated. Therefore, for each migration technique nearly constant total migration time is observed, irrespective of the number of hosts. Further, among all three techniques, OC has highest total migration time for any number of hosts, which is proportional to the amount of data it transfers. GMGD&#39;s total migration time is slightly higher than that of GMLD, approximately 4% higher for 12 hosts. 
     The difference between the total migration time of GMGD and GMLD can be attributed to the overhead associated with GMGD for performing deduplication across the hosts. While the migration is in progress, it queries with the deduplication server to read, or update the status of deduplicated pages. Such requests need to be sent frequently to perform effective deduplication. 
     2) Busy VMs: Table I shows that Dbench equally increases the total migration time of all the VM migration techniques as compared to their total migration time with idle VMs. However, a slight reduction in the total migration time is observed with an increasing number of hosts. With a lower number of hosts (and therefore a lower number of VMs), the incoming 1 Gbps Ethernet link to the network attached storage server might remain unsaturated, and therefore each Dbench instance can perform I/O at a faster rate compared to a scenario with more VMs, where the VMs must contend for the available bandwidth. The faster I/O rate results in higher page dirtying rate, resulting in more data being transferred during VMs&#39; migration. 
     C. Downtime 
       FIG. 6  shows that increasing the number of hosts does not have a significant impact on the downtimes for all three schemes. This is because each VM&#39;s downtime is initiated independently of other VMs. However, the downtime for OC is slightly higher, in the range of 250 ms to 280 ms. 
     D. Background Traffic 
     With the three-rack testbed used in the above experiments, the core links remain uncongested due to limited number of hosts in each source rack. To evaluate the effect of congestion at core links, for the remaining experiments a 2-rack topology was used, consisting of one source rack and one target rack, each containing 10 hosts. With this layout, migration of VMs from 10 source hosts is able to saturate the core link between the TOR switches. 
     The effect of background network traffic on different migration techniques was investigated. Conversely, the effect of different migration techniques on other network-bound applications in the cluster was compared. For this experiment, the 10 GigE core link between the switches was saturated with VM migration traffic and background network traffic. 7 Gbps of background dNetperf[2] UDP traffic was transmitted from the source rack to the target rack such that it competes with the VM migration traffic on the core link. 
       FIG. 7  shows the comparison of total migration time with UDP background traffic for the aforementioned setup. With an increasing number of VMs and hosts, the network contention and packet loss on the 10 GigE core link also increases. A larger total migration time for all three techniques was observed as compared to the corresponding idle VM migration times listed in Table I. However, observe that GMGD has lower total migration time than both OC and GMLD, in contrast to Table I where GMGD had higher TMT compared to GMLD This is because, in response to packet loss at the core link, all VM migration sessions (which are TCP flows) backoff. However, the backoff is proportional to the amount of data transmitted by each VM migration technique. Since GMGD transfers less data, it suffers less from TCP backoff due to network congestion and completes the migration faster.  FIG. 8  shows the converse effect, namely, the impact of VM migration on the performance of Netperf. With an increasing number of migrating VMs, Netperf UDP packet losses increase due to network contention. For 10 hosts, GMGD receives 13% more packets than OC and 5.7% more UDP packets than GMLD. 
     E. Application Degradation 
     Table II compares the degradation of applications running inside the VMs during migration using 10×4 configuration. 
     NFS I/O Benchmark: VMs images are often stored on a network attached storage, which can be located outside the rack hosting the VMs. Any I/O operations from VMs traverse one or more switches before reaching the storage server. Here the impact of migration on the performance of I/O operations from VMs in the above scenario is evaluated. Two NFS servers are hosted on two machines located outside the source rack, and each connected to the switch with 1 Gbps Ethernet link. Each VM mounts a partition from one of the NFS servers, and runs a 75 MB sequential file write benchmark. The migration of VMs is carried out while the benchmark is in progress, and the effect of migration on the performance of the benchmark is observed. Since, at the source network interface, the NFS traffic interferes with the migration traffic, the benchmark shows degradation proportional to the amount of data the migration technique transfers. Table II shows the NFS write bandwidth per VM. GMGD yields the smallest reduction in observed bandwidth among the three. 
     TCP RR: Netperf TCP RR VM workload was used to analyze the effect of VM migration on the inter-VM communication. TCP RR is a synchronous TCP request-response test. 20 VMs from 5 hosts are used. as senders, and 20 VMs from the other 5 hosts as receivers. The VMs are migrated while the test is in progress and measure the performance of TCP RR. Figures in Table II show the average transaction rate per sender VM. Due to the lower amount of data transferred through the source NICs, GMGD keeps the NICs available for the inter-VM TCP RR traffic. Consequently, it least affects the performance of TCP RR and gives the highest number of transactions per second among the three. 
     Sum of Subsets: is a CPU-intensive workload that, given a set of integers and an integer k, finds a non-empty subset that sum to k. This program is run in the VMs during their migration to measure the average per-VM completion time of the program. Although GMGD again shows the least adverse impact on the completion time, the difference is insignificant due to the CPU-intensive nature of the workload. 
     F. Performance Overheads 
     Duplicate Tracking: Low priority threads perform hash computation and dirty-page logging in the background. With 4 VMs and 8 cores per machine, a CPU-intensive workload (sum of subsets) experienced an 0.34% overhead and a write-intensive workload (random writes to memory) experienced a 1.99% overhead. With 8 VMs per machine, the overheads were 5.85% and 3.93% respectively, primarily due to CPU contention. 
     Worst-case workload: GMGD does not introduce any additional overheads, compared against OC and GMLD, when running worst-case workloads. VMs run a write-intensive workload that reduces the likelihood of deduplication by modifying 1.7 times as much data as the sire of each VM. All the three techniques show no discernible performance difference in terms of total migration time, data transferred, and application degradation. 
     Space overhead: In the worst case, when all pages are unique, the space overhead for storing the deduplication data structures in each host is 4.3% of the total memory of all VMs. 
     Hardware Overview 
       FIG. 8  (see U.S. Pat. No. 7,702,660, issued to Chan, expressly incorporated herein by reference), shows a block diagram that illustrates a computer system  400 . Computer system  400  includes a bus  402  or other communication mechanism for communicating information, and a processor  404  coupled with bus  402  for processing information. The processor may be a multicore processor, and the computer system may be duplicated as a cluster of processors or computing systems. Computer system  400  also includes a main memory  406 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus  402  for storing information and instructions to be executed by processor  404 . Main memory  406  also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  404 . Computer system  400  further includes a read only memory (ROM)  408  or other static storage device coupled to bus  402  for storing static information and instructions for processor  404 . A storage device  410 , such as a magnetic disk or optical disk, is provided and coupled to bus  402  for storing information and instructions. The computer system  400  may host a plurality of virtual machines (VMs), which each act as a complete and self-contained computing environment for the software and user interaction, while sharing physical resources. 
     Computer system  400  may be coupled via bus  402  to a display  412 , such as a liquid crystal display monitor, for displaying information to a computer user. An input device  414 , including alphanumeric and other keys, is coupled to bus  402  for communicating information and command selections to processor  404 . Another type of user input device is cursor control  416 , such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor  404  and for controlling cursor movement on display  412 . This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. In a server environment, typically the user interface for an administrator is provided remotely through a virtual terminal technology, though the information from the physical communications ports can also be communicated remotely. 
     The techniques described herein may be implemented through the use of computer system  400 , which will be replicated for the source and destination cluster, and each computer system  400  will generally have a plurality of server “blades”. According to one embodiment of the invention, those techniques are performed by computer system  400  in response to processor  404  executing one or more sequences of one or more instructions contained in main memory  406 . Such instructions may be read into main memory  406  from another machine-readable medium, such as storage device  410 . Execution of the sequences of instructions contained in main memory  406  causes processor  404  to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software. 
     The term “machine-readable medium” as used herein refers to any medium that participates in providing data that causes a machine to operation in a specific fashion, and is tangible and non-transitory. In an embodiment implemented using computer system  400 , various machine-readable media are involved, for example, in providing instructions to processor  404  for execution. Such a medium may take many forms, including but not limited to, non-volatile media, and volatile media, which may be local or communicate through a transmission media or network system. Non-volatile media includes, for example, optical or magnetic disks, such as storage device  410 . Volatile media includes dynamic memory, such as main memory  406 . Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus  402 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. 
     Common forms of machine-readable media include, for example, a hard disk or any other magnetic medium, a DVD or any other optical medium, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     Various forms of machine-readable media may be involved in carrying one or more sequences of one or more instructions to processor  404  for execution. For example, the instructions may initially be carried on a magnetic disk or solid state storage media of a remote computer. The remote computer can load the instructions into its dynamic memory. Bus  402  carries the data to main memory  406 , from which processor  404  retrieves and executes the instructions. The instructions received by main memory  406  may optionally be stored on storage device  410 . 
     Computer system  400  also includes a communication interface  418  coupled to bus  402 . Communication interface  418  provides a two-way data communication coupling to a network link  420  that is connected to a local network  422 . For example, communication interface  418  may be a 10 Gigabit Ethernet port to provide a data communication connection to switch or router. The Ethernet packets, which maybe jumbo packets (e.g., 8k) can be routed locally within a data center using TCP/IP or in some cases UDP or other protocols, or externally from a data center typically using TCP/IP protocols. Wireless links may also be implemented. In any such implementation, communication interface  418  sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. 
     Network link  420  typically provides data communication through one or more networks to other data devices. For example, network link  420  may provide a connection through local network  422  to a host computer  424  or to data equipment operated by an Internet Service Provider (ISP)  426  or to an Internet  428  backbone communication link. In the case where an ISP  426  is present, the ISP  426  in turn provides data communication services through the world wide packet data communication network now commonly referred to as the Internet  428 . Local network  422  and Internet  428  both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link  420  and through communication interface  418 , which carry the digital data to and from computer system  400 , are exemplary forms of carrier waves transporting the information. 
     Computer system  400  can send messages and receive data, including program code, through the network(s), network link  420  and communication interface  418 . In the Internet example, a server  430  might transmit a requested code for an application program through Internet  428 , ISP  426 , local network  422  and communication interface  418 . 
     The information received is stored in a buffer memory and may be communicated to the processor  404  as it is received, and/or stored in storage device  410 , or other non-volatile storage. 
     U.S. 2012/0173732, expressly incorporated herein by reference, discloses various embodiments of computer systems, the elements of which may be combined or subcombined according to the various permutations. 
     It is understood that this broad invention is not limited to the embodiments discussed herein, but rather is composed of the various combinations, subcombinations and permutations thereof of the elements disclosed herein, including aspects disclosed within the incorporated references. The invention is limited only by the following claims 
     REFERENCES 
     Each of the following references is each expressly incorporated herein by reference in its entirety. 
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                 TABLE I 
               
             
             
               
                   
               
               
                 Total migration time (in seconds) 
               
             
          
           
               
                   
                 Total migration time (seconds) 
               
             
          
           
               
                   
                 Idle VMs 
                 Busy VMs 
               
             
          
           
               
                 Hosts × VMs 
                 OC 
                 GMLD 
                 GMGD 
                 OC 
                 GMLD 
                 GMGD 
               
               
                   
               
             
          
           
               
                 2 × 4 
                 18.98 
                 10.96 
                 10.61 
                 25.87 
                 17.12 
                 17.40 
               
               
                 4 × 4 
                 18.23 
                 11.70 
                 11.8 
                 23.45 
                 15.64 
                 15.98 
               
               
                 6 × 4 
                 18.67 
                 11.21 
                 11.56 
                 21.97 
                 14.92 
                 15.07 
               
               
                 8 × 4 
                 18.26 
                 11.31 
                 11.25 
                 20.98 
                 14.13 
                 14.37 
               
               
                 10 × 4  
                 18.7 
                 11.16 
                 12.05 
                 21.90 
                 14.13 
                 14.9 
               
               
                 12 × 4  
                 19.10 
                 11.48 
                 12.00 
                 21.65 
                 14.05 
                 14.09 
               
               
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE II 
               
             
             
               
                   
               
               
                 Application degradation in migrating 40 VMs 
               
             
          
           
               
                   
                 W/o  
                   
                   
                   
               
               
                 Benchmarks 
                 Migration  
                 OC 
                 GMLD 
                 GMGD 
               
               
                   
               
             
          
           
               
                 NFS (Mbps/VM) 
                 48.08 
                 34.52 
                 36.93 
                 44.82 
               
               
                 TCP-RR (trans/sec) 
                 1180 
                 232.36 
                 280.41 
                 419.86 
               
               
                 Sum of Subsets (sec) 
                 32.32 
                 33.045 
                 33.77 
                 32.98