# Load Balancing Optimization of In-Memory Database for Massive Information Processing of Internet of Things (IoTs)

Authors: Ran Wei
Journal: Mathematical Problems in Engineering
 (2022)

---

## Abstract

Based on the analysis of the key technologies of the Internet of Things service platform architecture, a load balancing optimization scheme of in-memory database based on the massive information processing of the Internet of Things service platform is proposed. This scheme firstly proposes a system model that can satisfy the mass sensor information processing under the open platform environment and designs several functional unit modules of the system. By combining these functional units, the service can be configured for thousands of services and tenants. This paper presents an adaptive strategy selection method, which can automatically select the optimization strategy by dividing the position and querying the selection rate to improve the efficiency of the adaptive index algorithm. The index structure is initialized by parallel sorting algorithm, and the query statement is executed and the index structure is optimized by thread level parallel and radix sort methods. An elastic pipeline technique is proposed, which includes an elastic iterator model and a dynamic scheduler. The elastic iterator model is an upgrade of the traditional iterator model, adding the characteristics of dynamic multicore execution. In the process of query processing, dynamic scheduler monitors the load of each node in real time and dynamically adjusts the parallelism, so as to realize the load balance of in-memory database and maximize the utilization of hardware resources. The elastic pipeline realizes the isolation of parallelism from query compilation to avoid inappropriate parallelism allocation caused by missing and insufficient information during query compilation.


---

## Body

## 1. Introduction

Sensor information processing based on Internet of Things service platform faces multiple challenges. First of all, the amount of sensor information continuously produced is very large. With the growth of the Internet of Things, more and more services are integrated into the service platform, and more and more sensor information needs to be processed on the service platform, which is difficult to be supported by traditional processing methods. Second, the service platform has many types of business and involves a wide range of industries and different types of applications, so there are various types of sensing information. Thirdly, the service platform of the Internet of Things should be an open and integrated environment. Different services should share sensor information on the basis of ensuring information security, and sensor information from different sources can be processed in this platform. Therefore, the processing method and system design of massive sensor information are the key issues to be studied in the application deployment of Internet of Things.This paper transforms the service-oriented resource discovery problem into a search problem and proposes a biased random walk search algorithm [1, 2] in a unified wireless network, which is used to realize the resource allocation strategy among different services. In this algorithm, a randomly generated walk packet structure is emitted from the source node, and the next-hop node is randomly selected from the neighbor node until the packet arrives at the destination node. In this way, relevant resource information such as network can be obtained, and the optimal resource consumption path can be obtained when a hop is obtained through several rounds of iteration to provide corresponding services [3]. A cross-layer sleep scheduling algorithm is designed for service-oriented wireless sensor networks, which needs to interact with different service applications on the upper layer when it conducts sleep scheduling on nodes [4, 5]. Among them, modules such as energy management are also encapsulated as a service, which can dynamically change the sleeping strategy of nodes according to different service demands. This is a completely different new attempt to reduce the energy consumption of nodes by designing relevant energy saving strategies at the MAC layer of sensor network [6]. In this paper, the random distribution problem in service orientation under ubiquitous computing environment is studied, and the method to solve the np-complete problem is proposed. Meanwhile, the application conditions and the proof process of the solution are given [7, 8]. Sensor physical devices such as the various functions of abstraction and combination then packaged into different services, according to different service design standard unified management and integrated distribution platform; at the same time, they point out that such a service-oriented design can more quickly and easily build a variety of new applications for users [9]. An active identification mechanism for household appliances was designed for the household energy saving management system in the smart home, in which they proposed the concept of the intermediate management service layer of the Internet of Things [10]. In this layer, it not only realizes the function of establishing communication service between different electrical appliances, but also proposes service-oriented design architecture for the upper layer application [4]. Extensible network architecture for context awareness in pervasive computing is proposed. The architecture can meet the strict requirements of different data acquisition networks in the Internet of Things. In order to avoid the use of traditional hierarchical network structure [11, 12], the network architecture of streaming perceptive services in context-aware networks is aimed at pointing out the lack of universality and context-aware capability of the traditional Internet. Under the condition of mutual perception between people, machines, and things in the Internet of Things environment, this service-oriented streaming network architecture can be better applicable to sensors and item nodes, so it is more suitable to connect more common items in the edge network as terminals to the network environment [13, 14]. Since it was proposed, adaptive index technology [15] has attracted extensive attention and mainly solved the shortcomings of traditional indexes. Different from the traditional index, the core idea of adaptive index is to build and optimize the index structure dynamically and adaptively according to the query conditions, and the establishment of index is a part of the query task. This technique creates better indexes for rows that are frequently queried in the table and not indexes for rows that are not queried [16, 17]. Database Cracking algorithm [18] was first proposed as the core of adaptive index and has been widely used due to its high efficiency and simplicity. Currently, there is a lot of research in Database Cracking, which mainly focuses on the following three aspects: improving algorithm convergence speed, enhancing algorithm robustness, and improving algorithm effect by using multicore parallel [19, 20]. Buffered-Swapping algorithm [21, 22] stores the elements of two heap structures that need to be exchanged. When the filling order is exchanged, the convergence speed of the algorithm will be improved. DDR (Double Data Rate), DDC (Direct Digital Control), and other algorithms [23, 24] are proposed to solve the problem of sequential query style. DDR algorithm achieves good results. DDR divides the blocks again through a random fulcrum to improve the robustness of the algorithm and make it better adapt to the sequential query style. The rouge-index algorithm performs a range partition of the whole index before inquiry to improve the robustness, but the disadvantage of this method is that there is a longer initialization time. In the face of the new hardware architecture, there are not many studies on the multicore parallel adaptive indexing technology, and only some research results have certain deficiencies [25, 26]. How to utilize the multicore parallel resources efficiently and how to avoid the conflicts between threads are the key problems of multicore parallel computing. Conflicts between threads are avoided by means of locking, and three different types of locking methods are designed. However, frequent locking and unlocking operations consume a lot of time. Partition Merge algorithm is where all threads work together on a query task one at a time. Although this algorithm avoids frequent locking and unlocking operations, when all threads jointly process a small query task, the parallel resources of the processor cannot be utilized efficiently, which is likely to cause resource waste. Some in-memory databases have some disadvantages due to their own mechanism. In the process of massive user requests, the consistency hashing algorithm on the client is used to realize the distribution and play a certain load role. However, in this way, there is no communication between storage servers, and the distribution is completely dependent on its client, so there is a single point of failure problem for storage servers. At the same time, in the common application scenarios of memory cache system, read operation is far more than write operation, and the requirement for concurrent read processing is increasing. However, Memcached manages hashtables using a large lock mechanism. Each data operation locks the entire table, reducing the system’s ability to handle concurrent operations. In order to make the in-memory database have high availability and high concurrency, and better application in the cloud computing environment, a set of in-memory cache system is designed.In view of the heterogeneity and mass nature of data in the Internet of Things, this paper compares the differences between the data in the Internet of Things and the data in the traditional Internet. Based on the characteristics of Internet of Things data, the middleware framework of Internet of Things data processing based on SOA (Service-Oriented Architecture) is designed. Aiming at the unique semantic characteristics of IoT data, a service layer for semantic annotation of heterogeneous IoT data is designed in the SOA middleware framework. The efficiency of write operation is improved by caching. The improved load balancing of memory database for mass information processing on the Internet of Things supports dynamic memory allocation, which ensures that software can be used to merge write optimization and bypass cache optimization while improving access efficiency and reducing initialization overhead. In-memory database load balancing optimizations separate larger and smaller tasks by a threshold. For smaller tasks, each thread handles them independently, while for larger tasks, all threads work together to avoid thread waiting. Through experimental analysis, the optimization method used in this paper can effectively improve the efficiency of load balancing in memory database for parallel Internet of Things mass information processing and make it adapt to skewed data style.


## 2. Design of Mass Information Processing Framework for the Internet of Things Based on SOA

Currently, the proposed Internet of Things architecture is usually divided into four layers, namely, the perception layer, the network layer, the middleware layer, and the application layer, as shown in Figure1.Figure 1
Hierarchy of mass information processing in the Internet of Things.Wireless sensor node is one of the main components of sensor layer. Sensors can be used for a wide range of applications, such as light, temperature, humidity, air pressure, acceleration, sound, magnetic field, carbon dioxide concentration, and other physical properties. Physical quantities in the surrounding environment can be sensed by sensor nodes.


### 2.1. Design of Mass Information Processing Load Balancing Based on SOA Internet of Things

At the SOA service provider layer, this paper aims at the heterogeneity of internet-based data, realizes the integration task of heterogeneous data, and provides various applications with semantic annotation service design and semantic data mashup aggregation interface for heterogeneous data.Since the underlying devices of the Internet of Things are extremely rich, SOA systems that provide network services need to consider network transport issues, such as transmission delay and resource scheduling, as well as providing multiple routing or delay tolerance technologies to deal with in different network services. Different application platform requirements lead to a more common design pattern for SOA systems, and we will first consider the interaction of standard users between different devices and upper layers through different access platforms. Internet of Things memory load balancing hierarchy is shown in Figure2.Figure 2
Internet of Things memory load balancing hierarchy.


### 2.2. Semantic Annotation Service Design for In-Memory Database Load Balancing Optimization

The semantic annotation service for in-memory database load balancing optimization has four levels: data resource service, data flow management service, semantic tagging service, and semantic data manipulation service, as shown in Figure3.Figure 3
Load balancing optimization of in-memory data flow based on SOA Internet of Things.The topmost semantic data manipulation service provides the basic invocation service operation interface for the topmost applications. This layer service mainly uses semantic tags to carry out semantic annotation and connects with the database of related domain ontology to form a data set that meets the association relationship required by specific applications, so as to meet different requirements. In different applications, the service provides users with real-time data containing semantic information. Semantic data manipulation services that use this information can publish, store, and search semantic data and even aggregate dynamic real-time sensing data as needed, which is essential for most applications on the Internet of Things.Load balancing is mainly responsible for apportioning the user’s request pressure to reduce the processing pressure per unit time of each machine. Considering the high availability and high concurrency of the system, this paper adopts Keepalived and LVS (Linux Virtual Server) technology to build an LVS cluster.There are several roles in the cluster:(1)
Director Server: load scheduling balancer, mainly responsible for distributing user requests(2)
Real Server: background server, which is responsible for processing user requests(3)
VIP: IP address provided by the cluster to provide access services for users(4)
Master: the host in Keepalived can handle computing work when working normally(5)
Backup: Keepalived can continue to provide service in case of host malfunctionThe actual machine information corresponding to each model is shown in Table1.Table 1
Load balancing scheduling table.
File sizeData volume (10,000 pieces)Cluster (seconds)Single machine (seconds)350 MB163566650 MB3684751500 MB6311071212050 MB12591242153050 MB1887153372To see the results more intuitively, we represent Table1 as a compass.It can be seen from Figure4 that the in-memory database load balancing with massive information of the Internet of Things has obvious advantages in processing large data sets, which can significantly save working time and improve working efficiency, and the larger the data set, the more significant the advantages.Figure 4
Comparison results.


## 2.1. Design of Mass Information Processing Load Balancing Based on SOA Internet of Things

At the SOA service provider layer, this paper aims at the heterogeneity of internet-based data, realizes the integration task of heterogeneous data, and provides various applications with semantic annotation service design and semantic data mashup aggregation interface for heterogeneous data.Since the underlying devices of the Internet of Things are extremely rich, SOA systems that provide network services need to consider network transport issues, such as transmission delay and resource scheduling, as well as providing multiple routing or delay tolerance technologies to deal with in different network services. Different application platform requirements lead to a more common design pattern for SOA systems, and we will first consider the interaction of standard users between different devices and upper layers through different access platforms. Internet of Things memory load balancing hierarchy is shown in Figure2.Figure 2
Internet of Things memory load balancing hierarchy.


## 2.2. Semantic Annotation Service Design for In-Memory Database Load Balancing Optimization

The semantic annotation service for in-memory database load balancing optimization has four levels: data resource service, data flow management service, semantic tagging service, and semantic data manipulation service, as shown in Figure3.Figure 3
Load balancing optimization of in-memory data flow based on SOA Internet of Things.The topmost semantic data manipulation service provides the basic invocation service operation interface for the topmost applications. This layer service mainly uses semantic tags to carry out semantic annotation and connects with the database of related domain ontology to form a data set that meets the association relationship required by specific applications, so as to meet different requirements. In different applications, the service provides users with real-time data containing semantic information. Semantic data manipulation services that use this information can publish, store, and search semantic data and even aggregate dynamic real-time sensing data as needed, which is essential for most applications on the Internet of Things.Load balancing is mainly responsible for apportioning the user’s request pressure to reduce the processing pressure per unit time of each machine. Considering the high availability and high concurrency of the system, this paper adopts Keepalived and LVS (Linux Virtual Server) technology to build an LVS cluster.There are several roles in the cluster:(1)
Director Server: load scheduling balancer, mainly responsible for distributing user requests(2)
Real Server: background server, which is responsible for processing user requests(3)
VIP: IP address provided by the cluster to provide access services for users(4)
Master: the host in Keepalived can handle computing work when working normally(5)
Backup: Keepalived can continue to provide service in case of host malfunctionThe actual machine information corresponding to each model is shown in Table1.Table 1
Load balancing scheduling table.
File sizeData volume (10,000 pieces)Cluster (seconds)Single machine (seconds)350 MB163566650 MB3684751500 MB6311071212050 MB12591242153050 MB1887153372To see the results more intuitively, we represent Table1 as a compass.It can be seen from Figure4 that the in-memory database load balancing with massive information of the Internet of Things has obvious advantages in processing large data sets, which can significantly save working time and improve working efficiency, and the larger the data set, the more significant the advantages.Figure 4
Comparison results.


## 3. Load Balancing Optimization of In-Memory Database for Mass Information Processing in Internet of Things

The storage structure of partition results directly affects the efficiency of the whole partition algorithm. In traditional methods, a vector container or array is often used to store elements in a partition. The use of vector container supports dynamic memory allocation. However, when the number of elements in the vector container increases, in order to ensure the physical continuous storage of the stored elements, the vector container will carry out memory opening, element replication, and other operations, which greatly affects the efficiency of the container. The advantage of using arrays is to ensure that the elements are stored continuously and no replication occurs, but arrays belong to static memory allocation, so the number of elements in each partition needs to be calculated in advance, and the overhead of initializing a large amount of memory at one time is also high.An array of pointers of length is equal to the number of partitions (called a position array), with each pointer pointing to the location of the current space node in the partition, the current storage location. Using improved memory database of internet of mass information processing load balance, the partition algorithm first writes data to each of the division of the cache area, when the data is stored in the buffer space, a new node and the node address are used to enter the buffer. The whole cache writes the data directly to the memory through nontemporary writing operations. Since each node is 64B in size, it is possible to write the cached data to memory through two M256_streams. Finally, the address information in the cache is updated to the location array for the next write operation. The specific algorithm flow is shown in Table2. Improved in-memory database load balancing for mass information processing on the Internet of Things supports dynamic memory allocation to ensure that software can be used to merge write optimization and bypass cache optimization at the same time. It improves access efficiency and reduces Table 3initialization overhead.Table 2
Hash partition.
Input: Data to be divided inputData[]Loop:Step1: Process a new element, carry out load balancing calculation of in-memory database for mass information processing of Internet of Things based on element value, and determine the partition resultStep2: Write the element into the cache area to which it belongsUntil: 56B storage space in a partitioned cache is fullEnd loopStep3: Create a new node and store its address in the divided cache area, so that the storage space of 64B in the cache area has been filled upStep4: Directly write the data of 64B in the partition cache area into the memory address indicated by the partition in the location array. The procedure uses a nontemporal writing operation, bypassing the cache, and writing directly to memoryStep5: Update the memory address referred to by this partition in the location array to the memory address of the new node in the cacheUntil: All elements are processedEnd loopTable 3
Load balancing optimization algorithm for in-memory database.
Step1: Set a threshold valueT to compare the size of Q partition results generated from the first partitionStep2: In the second division process, the thread first compares the size and thresholdT of the task before processing it. If the task is less than T, then the thread processes the task. If the task is greater than T, it is queued for D and not processed. So far, tasks less than T have been processed, while tasks greater than T have not been processed and are stored in DStep3: Divide each task in queueD into T parts on average (T is the number of Reduce threads), and assign R parts of tasks to R threads for parallel processingStep4: Get the final partition result after processing each task in queueDFirst, the input data is divided into several blocks according to the number of threads, and each piece of data is given to a thread for processing. All threads perform the first partition in parallel, producingQ1 partition results. In the first partition, there are conflicts between threads that are resolved by the proposed policy. Because the input data is evenly divided, the amount of data processed by each thread is basically the same in the first partition, and the phenomenon of thread waiting does not occur. In the second partition, each partition result of the first partition is divided into Q2, and the final Q1∗Q2 partition result is obtained. In the second partition, one thread processes one of the Q1 partition results, and all threads execute in parallel. Since one of the final Q1∗Q2 partition results can only come from one of the P1 partition results of the first partition, there is no conflict between threads in the second partition. However, because the input data may be skewed, the Pl partition results of the first partition may not be the same, which will lead to the problem of unbalanced load in the second partition and the situation of thread waiting, which will affect the efficiency of the algorithm.In multistep partitioning, the thread load imbalance during the second partitioning may occur becauseP1 partition results from the first partitioning may not have the same size. To solve this problem, this chapter proposes load balancing optimization. Set a threshold S to compare the size of each partition result from the first partition. If the size of the partition result D is less than S, the second partition will proceed normally; if it is greater than S, the partition result will be left untreated. Finally, all partitions greater than S are executed in parallel with all threads. See Table 3.When the top stack is full, put the top elements at the highest altitude and so on until there are no elements in the top stack. By using this method, the relative order of elements can be kept to the greatest extent, the convergence speed can be accelerated, and the operation of element exchange in subsequent queries can be reduced, so as to improve the efficiency of subsequent queries. The speed of convergence can be controlled by adjusting the size of the heap. Buffered-Swap Cracking becomes full sorting when the size of the heap is equal to the total number of elements exchanged in division: Buffered-Swap Cracking becomes standard when the size of the heap is equal to 1. Therefore, the larger the heap size, the faster the convergence, and the higher the cost of maintaining heap order. The smaller the heap size, the slower the convergence rate, and the lower the overhead of keeping the heap in order. In a tilted query style, some blocks are often queried and some blocks are not. For different blocks, the heap size of buffer exchange is fixed in the cracking algorithm. This makes it easy to use a larger heap structure to handle the less important blocks of data and can cause additional waste, especially for the skewed query style. To solve this problem, this paper proposes an improved method. The core idea of this method is as follows: in the skew query style, for the important data blocks, the use of a larger heap structure speeds up the convergence rate; for nonessential data blocks, use a smaller heap structure, reducing the overhead required to maintain the order of elements within the heap. According to the number of times the data block is queried, the size of heap structure is dynamically adjusted to better adapt to the skew query style.Tree node means that all data starting at positionY in the index is greater than X. In the improved method, a Z attribute is added to the tree node. Attribute Z represents the number of times the first block from position Y has been queried. When executing the query statement (a, b), the starting and ending position of the data block containing z elements are found in the tree structure as heap size, to build a big head and a little heap. Then, [Yi, Y2 − 1] is used as a fulcrum for Buffered-Swap Cracking algorithm in the division operation, and the final position of the division is Ya. Finally, the starting node is updated, and the new node is inserted into the tree structure. Another query boundary B is processed in the same way, and the new node is also inserted into the tree structure to get the final query result. In the improved method, the attribute Z is set to an initial value of 1, indicating that the standard Database Cracking algorithm is used when the block is first queried. As the query progresses, the attributes of Zvalue increase, and Buffered-Swap Cracking algorithm is used. In the improved approach, you can set an upper limit on attribute Z to prevent the heap structure from becoming too large. In addition, you can improve the blend of Buffered-Swap and standard database decryption algorithms, using the improved Buffered-Swap algorithm before multiple queries and the standard database decryption algorithm after.


## 4. Example Verification

Two data sets are used in this experiment: one is the uniformly distributed data set (denoted byA); the other is a data set with data skew (denoted by B). The two data sets contain 108 tuples, in which the size of each tuple is 16B, the number is 8B, and the partition value is 8B. This tuple structure is often used in column storage databases. For data sets with data skew, Zipf index is used to measure the skew degree.Figure5 shows the experimental results of single-step load balancing of in-memory database with four different write strategies. Data set A was used in the experiment, and the aforementioned optimization method was adopted. In the locking strategy, when the partition bit is small, there are more tuples in each partition result, and frequent locking and unlocking operation will affect the overall performance. As partition bits increase, the number of tuples per partition result decreases, conflicts between threads decrease, and overall performance improves. As partition bits continue to increase, cache loss and TLB loss can affect program performance. In the independent space strategy, because there is no lock operation, the performance of the program is much better than that of the locking strategy when the partition bit is small. Since the program requires many additional variables to record the current write location, partition size, and other information, and the number of these variables increases with the number of threads, the memory pressure borne by the independent space strategy increases with the increase of the Hash Bit. In addition, considering the impact of cache loss and TLB miss, the overall performance of the program decreases significantly with the increase of partition bits. The same analysis applies to the twice-traversal policy and the parallel cache policy. Therefore, when the partition bit is large, the locking strategy is superior to the other three strategies, which indicates that the impact of cache loss and TLB miss when the partition bit is large is greater than the impact of lock unlock operation. It should be noted that the overall performance of the program in the two-pass strategy is largely limited to the operation of calculating the write position. Figure 6 shows the comparison of experimental results between the proposed algorithm and the traditional in-memory database load balancing algorithm. The results of the traditional in-memory database load balancing algorithm in the figure are from literature [25]. Data set A was used in the experiment to carry out single-step load balancing of in-memory database under lock-in strategy and lock-free strategy. Experimental results show that the proposed algorithm is more effective than the traditional in-memory database load balancing algorithm.Figure 5
Load balancing partitioning results of four different write strategies.Figure 6
Comparison of load balancing algorithm and no-load balancing algorithm.Taking data set A as input, the effect of nontemporal writing operation on the load balancing algorithm of the in-memory database with massive information on the Internet of Things was studied. The experimental results are shown in Figure7. In this experiment, 8 threads are used to perform load balancing on the in-memory database of massive information of Internet of Things in parallel, and the conflicts between threads are solved by the strategy of twice traversal. As can be seen from the figure, using nontemporal writing operation to bypass the cache and directly write the data into memory can effectively improve the efficiency of the algorithm. As can be seen from the figure, the optimization is better for partition operations with a large number of partitions (with a relatively high promotion). When the number of partitions is large, the number of cache areas increases, so do other variables related to partitions (free location information, partition size information, etc.), which all put more pressure on memory. Therefore, as the number of partitions increases, the number of cache misses also increases, so the improvement of algorithm performance by bypassing cache optimization is more obvious.Figure 7
Bypass cache optimization experiment results.The method proposed in this paper was compared with Database Cracking algorithm and full index algorithm by using the random query style and 1% selection rate. The experimental results are shown in Figure8. The x-coordinate in the figure represents the number of queries and the y-coordinate represents the time taken for the query, which does not include the time taken to output the query results. The full index algorithm in this experiment is composed of speed sorting algorithm and binary search algorithm. It can be seen from the figure that (1) the full index algorithm has the best convergence speed, but its initialization time is extremely high, because all data should be sorted completely; (2) Database Cracking achieved good results in a random query style, maintaining a low initialization overhead and a good speed of convergence; (3) the method proposed in this paper is superior to the Database Cracking algorithm in most queries, which verifies the effectiveness and advancement of the self-adaptive selection optimization method proposed in this paper.Figure 8
Comparison of load balancing performance of in-memory database for mass information processing in the Internet of Things.For nonpartitioned in-memory database load balancing connections, you can see that the elapsed time decreases as the skew increases, partly due to the probe process, and the build process remains the same because theR relationship does not change. The main reasons are as follows: (1) the nonpartitioned hash connection can ensure that the input data is evenly distributed according to the number of threads during the probe, regardless of the skew of the input data. Partitioning hash connection requires partitioning operation, which is easy to cause load imbalance for skewed data. (2) Data skew reduces the number of cache misses during the probe because tuples that are often matched in the R relationship are retained in the cache, increasing the cache hit ratio. The more skewed the data, the better.


## 5. Conclusion

Before the solution is proposed in this paper, a clear understanding of sensor information processing is required. Firstly, the architecture of the service platform of the Internet of Things should be considered, and the characteristics of openness, big data, and multitenancy of sensor information processing in the service platform should be clarified. On this basis, a theoretical model of the sensor information processing system should be established. Then, based on the theoretical model, the load balancing method of in-memory database for mass information processing of the Internet of Things is adopted to realize distributed and parallel computing and realize efficient processing of mass sensor information. It can effectively reduce the number of TLB misses: bypass the cache optimization and directly write the data no longer used in the short term into the corresponding memory address through nontemporal writing operation, so as to avoid cache and improve the efficiency of write operation. The improved load balancing of memory database for mass information processing on the Internet of Things supports dynamic memory allocation, which ensures that software can be used to merge write optimization and bypass cache optimization while improving access efficiency and reducing initialization overhead. Load balancing optimization separates the larger tasks from the smaller tasks by a threshold. For the smaller tasks, each thread works independently, while for the larger tasks, all threads work together to avoid the occurrence of thread waiting. Through experimental analysis, the optimization method used in this paper can effectively improve the efficiency of load balancing in memory database for parallel Internet of Things mass information processing and make it adapt to skewed data style. For a memory cache system, data is stored in memory. If an exception occurs, such as a power failure, data may be lost. The next step persistence technology can periodically store data to hardware devices such as disks to ensure data security.



---

*Source: 9138084-2022-05-11.xml*