## 1 Introduction  
High Performance Computing (HPC) data centers typically contain a large number of computing nodes each consisting of multiple processing cores. The size and performance of these systems continue to increase which causes concerns to be raised over higher energy requirements [1; 2; 3; 4]. Research estimates that data centers worldwide account for 1.1-1.5% of global electricity use [5]. It is therefore important to take measure that reduce this energy consumption. Different techniques have been proposed in the literature to improve the energy efficiency of the data center. Dynamic Power Management (DPM) and Dynamic Voltage Frequency Scaling (DVFS) are popular techniques to reduce the power consumption of under-utilized resources. Consolidation of virtual machines (VMs) running in different servers into fewer servers to enable aggressive DPM or DVFS has become a major focus area in the research community [6; 7; 8; 9; 10; 11; 12; 13]. However, server-consolidation with the sole objective of power reduction can impact the performance of the HPC data center negatively if the network is incapable of supporting the resultant aggregated traffic patterns or hotspot scenarios [14]. Therefore, careful attention to the impact of consolidation on network performance is necessary. HPC data centers maintain queues of jobs which arrive periodically and must schedule these jobs to be executed in order to produce a profit. It is common to assign values to jobs which imply their level of importance compared to other jobs: higher value equates to higher importance [15]. Value is typically assigned based on expected profit earned from the completion of the job. In HPC systems, the scheduling of jobs is influenced by their value; typically a resource management system will attempt to maximize its profits by allocating its limited resources to the highest-value jobs in the queue. This is especially true when jobs arrive at a rate higher than the rate at which the system can process and execute them. Typically, the value obtained by completion of a job is time-dependent which reflects the necessity to schedule as early as possible. Existing approaches which optimize resource management use fast heuristics to very quickly find practical allocations for the dynamically arriving jobs. The use of heuristics over more complicated algorithms reduces overhead in the delay of allocations and also lowering the resource requirement of the resource management system itself. Research has also been conducted which considers profiling results from design-time testing to improve both the run-time computational complexity and the quality of the heuristics [16; 17]. While the results of these researches show significant improvement over other approaches, the technique is only applicable in select situations due to the required accurate information and assumption that there is little deviation in resources required by the jobs in the system. The challenge to overcome these disadvantages is to design an algorithm that accounts for this variation and builds up a full history of information about jobs in real-time instead of requiring the information to exist already. In an orthogonal direction, novel data center network (DCN) technologies, leveraging emerging interconnection paradigms such as millimeter-wave (mmWave) interconnects have been proposed to reduce the power consumption of the networking equipment [18; 19]. Wireless data center architectures have been proposed where Top-of-Rack (ToR) switches are interconnected with mmWave links while the intra-rack communication is achieved through traditional Ethernet [20; 21; 22; 23]. Alternatively, server-centric wireless DCNs where direct wireless links are used for server-to-server communication have also been designed [24; 25]. These wireless data center architectures can be considered as viable alternate for traditional wired architecture for HPC computing for reducing even more power consumption.