As customers take virtual machines (VMs) as their demands, high-efficient placement of VMs is required to reduce the energy consumption in data centers. Existing Virtual Machine placement (VMP) strategies can minimize energy consumption of data centers by optimizing resource allocation in terms of multiple physical resources (e.g., memory, bandwidth, CPU, etc.). However, these strategies ignore the role of heat recirculation in the data center, which can cause a huge energy waste in cooling. To address this problem, we propose a heat-recirculation-aware VMP strategy for reducing the energy consumption of data centers. This novel VMP strategy takes into account heat recirculation coupled with multiple physical resource allocation to reduce the energy consumption of data centers. We design a simulated annealing based algorithm called SABA to lower the energy consumption of data centers where multiple VMs are deployed. SABA remarkably cuts down the energy consumption of physical resources through two salient features. First, it obtains an approximation of the optimum with much fewer iterations than simulated annealing algorithm (SA). Second, it reduces the number of activated servers required for VM tasks. We quantitatively evaluate the performance of SABA in terms of algorithm efficiency, the number of activated servers and the energy-saving. We compare the performance of SABA with state-of-art XINT-GA, PPVMP, TSTD and SA algorithms. Moreover, we evaluate the efficiency of SABA by leveraging a real-world 50 hours trace from practical IBM cloud data centers. Experimental results indicate that our heat-recirculation-aware VM placement strategy provides a powerful solution for improving the energy efficiency of data centers (SABA improves energy efficiency of cooling by up to 13.2% over TSTD, 13.8% over XINT-GA and 45% over PPVMP algorithm).

Instantiated containers of an application are distributed across multiple Physical Machines (PMs) to achieve high parallel performance. Container placement plays a vital role in network traffic and the performance of containerized data centers. Existing container placement techniques are inadequate due to the ignorance of container traffic patterns. To solve this issue, we first investigate the network traffic between containers and observe that it exhibits a Zipf-like distribution. Motivated by this finding, we propose a novel container placement approach- Blender -by taking into account the Zipf-like distribution. Blender employs two algorithms called RefineAlg and SplitAlg to divide containers of applications into blocks, and place these blocks across Virtual Machines (VMs). Blender exhibits two salient features: (i) it minimizes inter-block traffic by arranging the containers that communicate frequently in the same block. (ii) it achieves good load balancing by combining complementary blocks that request different resource types (e.g., CPU-intensive and memory-intensive blocks) and distributing these blocks across multiple VMs. The experimental results show that Blender significantly reduces communication traffic and network latency. In particular, Blender reduces the traffic of SBP and CA-WFD by 22% and 32%, respectively. Blender decreases network latency by 16% and 26% compared to SBP and CA-WFD. Furthermore, with Blender in place, the physical resources of hosting PMs are well balanced and utilized.

With the rapid growth of cloud computing, frequent workload bursts show an increasing influence on the Quality of Service (QoS) and energy efficiency of cloud-based data centers. Existing virtual machine placement schemes are expected to optimize either QoS or energy efficiency for cloud data centers running under bursty workload conditions. To bridge this gap, we propose a burst-aware and thermal-efficient virtual machine placement technique called BTVMP . BTVMP adopts a two-step strategy to achieve energy efficiency while assuring QoS. First, BTVMP leverages a split-and-recombine algorithm – SAR – to deal with bursty workloads. SAR prioritizes critical workloads while preventing low-priority workloads from starvation, thereby assuring QoS. Second, BTVMP utilizes an enhanced simulated annealing algorithm called ESA to offer optimal thermal-efficient virtual machine placement (VMP) solutions, aiming to minimize the energy consumption of data centers. To facilitate estimating energy consumption, we integrate into BTVMP a thermal model that takes into account heat re-circulation effects. We conduct extensive experiments with a real-world trace. We compare BTVMP with the leading-edge VMP strategies, including Genetic Algorithm (XINT-GA), Power-Aware and Performance-Guaranteed Virtual Machine Placement (PPVMP), Peak Load Scheduling Control Method (PLSC), First Come First Serve (FCFS), and GReedy based scheduling Algorithm miNImizing Total Energy (GRANITE). The experimental results unveil that BTVMP not only enhances QoS but also exhibits superb energy efficiency. In particular, BTVMP reduces PLSC's workload delay and FCFS's critical workload delay by 18 $\%$ and 11 $\%$ , respectively. Moreover, BTVMP lowers the total energy consumption of the three alternative algorithms –GRANITE, XINTGA, PPVMP, and PLSC – by anywhere between 27.8 $\%$ and 49.4 $\%$ .

With the explosive data growth, an enormous number of computing and networking components (e.g., servers, switches, and wires) are continuously being augmented to data centers. Data center networks (DCNs) -maintaining a high network capacity -must be cost efficient, incrementally scalable, and fault-tolerant. To address these challenges, we propose in this study a new type of DCN architecture referred to as Criso. Different from the existing network architectures, Criso is designed hierarchically and recursively by employing two ports servers and commodity switches. Criso is constructed based on numerous isomorphic pods, each of which leverages external interfaces supplied by switches to connect with neighboring pods. Additionally, a pod-based and fault-tolerant routing algorithm is designed to handle multiple failures. Criso has an array of promising features, including being cost-efficient and delivering a high-network capacity that can be extended to millions of nodes. The analytic results demonstrate that Criso is significantly superior to the four state-of-the-art data center structures in terms of network capacity, scalability, cost, power consumption, and other static characteristics. Furthermore, the experimental results unveil that Criso satisfies the fault-tolerant demands of modern data centers. Compared to the four existing topologies (i.e., DCell, BCube, FiConn, Fat-Tree) that have been widely investigated, Criso is adroit at maintaining a balanced performance in terms of throughput and latency.

The expansion of data centers, driven by the continuous development of network services, has led to a significant issue of high energy consumption. Due to the real-time interaction between IT and non-IT equipments, it is difficult to consider the holistic energy consumption of heterogeneous data centers. Therefore, this paper proposes a holistic-energy-aware-virtual machine placement (VMP) strategy for data centers that use heterogeneous resources to provide services. Firstly, we propose the energy-aware VMP strategy by using the probabilistically determining mechanism to effectively minimize the number of activated servers and improve server resource utilization. Secondly, within this strategy, we leverage dynamic voltage and frequency scaling (DVFS) technology, enabling nodes to operate at lower frequencies and voltages while meeting performance requirements, thus further reducing computing energy consumption. Thirdly, in addition to the previous two points, the probabilistic determined genetic algorithm (PDGA) is proposed to reasonably distribute the workloads based on the heat-recirculation effect and reduces the cooling energy consumption. The above mechanisms collectively optimize the global energy consumption of heterogeneous data centers. Experimental results demonstrate that the PDGA can significantly reduce the energy consumption of IT and non-IT equipment. The total energy consumption of the data center is significantly reduced (the PDGA is 20.83% lower than the simulated annealing based algorithm and 20.76% lower than the big data task scheduling algorithm based on thermal-aware and DVFS-enabled techniques).

Fault diagnosis of processors plays a critical role in assessing the reliability of multiprocessor systems. The interconnection network’s diagnosability is an important metric for measuring its self-diagnostic capability which has been extensively studied in many novel mutiprocessor systems. Permanent fault diagnosability for many mutiprocessor systems has been determined; however intermittent fault diagnosability is hard to obtain due to its crypticity. In this paper, we focus on the problem pertaining to the diagnosability in the intermittent fault situation. First, by learning the characteristics of intermittent fault diagnosis in PMC model, we propose some theorems and lemmas for intermittent fault diagnosability. Secondly, we give the range of intermittent fault diagnosability of product network. Lastly, by adopting the theorems, we propose an Auto-IFD algorithm to find the intermittent faults of the hypercube network and conduct experiments to verify the theorems.