Fault diagnosis technology is a method for locating faulty processors in multiprocessor systems, and it plays a crucial role in ensuring system stability, security and reliability. A widely used approach in this technology is the system-level strategy, which determines processor status by interpreting the set of test results between adjacent processors. Among them, thePMCandMMmodels are two commonly employed methods for generating these results. The diversity and complexity of network topologies in systems constrain existing algorithms to specific topologies, while the limitations of fault diagnosis strategies lead to reduced fault tolerance. In this paper, we present a novel path-based method to tackle the fault diagnosis problems in various networks according to the PMC and MM models. Firstly, we introduce the algorithm for partitioning the path into subpaths based on these models. To ensure that at least one subpath is diagnosed as fault-free, we derive the relationship between the fault bound$T$and the path length$N$. Then, building on methods for recognizing the subpath states, we have developed fault diagnosis algorithms for both the PMC and MM models. The simulation results show that our proposed algorithms can quickly and accurately diagnose faults in multiprocessor systems.

Escalating host temperatures in data centers can create hot spots, significantly boosting cooling costs and impacting reliability. Accurately predicting host temperatures is of the utmost importance in managing resources effectively. Existing temperature estimation solutions are inefficient due to a lack of accurate prediction. To this end, we proposed a deep learning-based thermal prediction approach called DLTPA, which aims to minimize the temperature and power consumption of virtual machines (VMs) in data centers. Specifically, we built a Long Short-Term Memory (LSTM) network model to accurately predict host temperature and power consumption, demonstrating superior performance over traditional algorithms. Our LSTM model exhibits exceptional accuracy in predicting thermal and energy dynamics, achieving an R2value of 0.98 in power consumption prediction, indicating exact forecasts. Furthermore, we design an efficient VM placement strategy to achieve host peak temperature reduction by rationally arranging VM tasks. The experimental results demonstrate that the DLTPA significantly improves over other leading-edge algorithms. It reduces peak power consumption by 3.64% to 9.39%, lowers average temperature by 3.21% to 7.96%, achieves a 0% SLA violation rate, and maintains a high level of load balancing at 19.8%.

With the rise of cloud computing and artificial intelligence, the resource utilization rate of data centers is soaring, resulting in significant power consumption and electricity costs. Traditional inappropriate resource placement methods cannot match the local electricity pricing form well, leading to data centers consuming a large amount of energy and requiring large electricity bills. In this paper, firstly, this paper study a global electricity-cost-minimization model, which takes into account both IT and non-IT resource usage. Secondly, We introduce a two-step algorithm (SAT-GAV) to solve the NP-hard TVMP problem. The first step of SAT-GAV uses the simulated annealing algorithm to arrange task placements to reduce electricity costs. In the second step, we uses the greedy algorithm to place virtual machines by leveraging of minimizing data center electricity costs. We conduct extensive experiments to evaluate the effectiveness of SAT-GAV. And compare the performance of our SAT-GAV with other state-of-art algorithms. Experimental results indicate that, compared to other algorithms, our VMP strategy can reduce the electricity costs by 8.9% compared to the best-performing PDGA algorithm and by 33.34% compared to the earlier XINT-GA algorithm.

Abstract 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.

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).

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 calledBTVMP. 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 calledESAto 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$\%$.

In the operation of large multiprocessor systems, intermittent faults have become an important reliability challenge due to their cryptic nature. In these systems, the occurrence of intermittent faults often affects the reliability of the system and disrupts the daily operation of the system. Existing studies have been able to determine the intermittent fault diagnosability of some crisp three-cycle networks, but there is still no effective method for determining the intermittent fault diagnosability and fault node confirmation of crossed cubes, twisted cubes and locally twisted cubes. Therefore, in this paper, we study the intermittent fault diagnosability of these three cubes. We propose theorems and lemmas to prove the intermittent fault diagnosability of n dimension cubes are di(CQn) = di(TQn) = di(LTQn) = n − 1, where n ≥ 3. Furthermore, we conduct experiments and implement a fault diagnosis algorithm to demonstrate our results.

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 calledRefineAlgandSplitAlgto 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-intensiveandmemory-intensiveblocks) 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.

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 do not consider the container traffic pattern, which is inadequate. To resolve this conflict, we investigate network traffic between containers and observe that it exhibits a Zipf-like distribution. We propose a novel container placement approach - Blender - by leveraging the Zipf-like distribution. Based on network traffic correlation, Blender employs RefineAlg and SplitAlg to divide containers of applications into blocks, and place these blocks across virtual machines. 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 blocks according to the resource types they require and distributing them across multiple PMs. We compare Blender against two state-of-the-art methods SBP and CA-WFD. The experimental results show that Blender significantly reduces communication traffic. In particular, for the same number of PMs, Blender reduces the traffic of SBP and CA-WFD by 22% and 32%, respectively. Furthermore, with Blender in place, the physical resources of hosting PMs are well balanced and utilized.

Summary With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow‐based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model.

Abstract In this study, we propose a caching method called RUE for dynamic large‐scale data streams. We define a data model to facilitate hot data identification and management. At the heart of RUE model is hot degree that takes into account two factors data resource utilization efficiency and reuse distance, aiming to quantitatively reflect data popularity in a dynamic data stream. Based on data's hot degree, RUE classifies data into four types, each of which is assigned with an associated cache residence time. Guided by RUE model, we develop HM algorithm to identify and manage hot data in a dynamic data stream. HM algorithm is implemented by four stacks, namely, new stack, short stack, long stack, and temp stack. Moreover, an eviction and a migration algorithms are integrated into HM to facilitate block replacement and migration. To evaluate the performance of HM algorithm, we quantitatively compare the performance of RUE with three state‐of‐art algorithms, namely, LRU, LIRS, and ARC under various replacement policies, operations, and workloads. Experimental results show that RUE outperforms these three existing algorithms in terms of both read and write hit rates. Furthermore, we show that with the four stacks in place, the computing overhead of HM is negligible.

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 asCriso. Different from the existing network architectures,Crisois designed hierarchically and recursively by employing two ports servers and commodity switches.Crisois constructed based on numerous isomorphicpods, each of which leverages external interfaces supplied by switches to connect with neighboring pods. Additionally, apod-based and fault-tolerant routing algorithm is designed to handle multiple failures.Crisohas 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 thatCrisois 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 thatCrisosatisfies 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,Crisois adroit at maintaining a balanced performance in terms of throughput and latency.

Data centers consisted of a great number of IT devices (e.g., servers, switches and etc.) which generates a massive amount of heat emission. Due to the special arrangement of racks in the data center, heat-recirculation often occurs between nodes. It can cause a sharp rise in temperature of the equipment coupled with local hot spots in data centers. Existing VM placement strategies can minimize energy consumption of data centers by optimizing resource allocation in terms of multiple physical resources (e.g., memory, bandwidth, cpu and etc.). However, existing strategies ignore the role of heat-recirculation in the data center. To address this problem, in this study, we propose a heat-recirculation-aware VM placement strategy and design a Simulated Annealing Based Algorithm (SABA) to lower the energy consumption of data centers. Different from the existing SA algorithm, SABA optimize the distribution of the initial solution and the way of iteration. We quantitatively evaluate SABA’s performance in terms of algorithm efficiency, the activated servers and the energy saving against with XINT-GA algorithm (Thermal-aware task scheduling Strategy), FCFS (First-Come First-Served), and SA. Experimental results indicate that our heat-recirculation-aware VM placement strategy provides a powerful solution for improving energy efficiency of data centers.

With the data growing explosively, data center networks (DCN) have to possess the characteristics of incrementally scalable, cost-efficient, high network capacity and fault tolerance. However, the widely used DCNs can not meet the demands above. In this paper, we propose a new type of data center topology named Criso to settle the challenges. Different from the existed works, Criso has the advantages of both switch-centric topologies (servers do not participate in routing) and the server-centric topologies (the scalability is not limited by the ports of switches). It is constructed based on pods, The internal structure of each pod is the same and there are only four external interfaces. By applying such structure, a pod-based and fault-tolerant routing algorithm is designed to handle multiple types of failures. Criso is hierarchically, recursively defined and high-network capacity which can scale up to millions of nodes. The analysis results demonstrate that the Criso model is significantly superior to four state-of-the-art data center structures in terms of the network capacity, scalability, cost, power consumption and other static characteristics. Criso achieves the target of low-cost, low-energy consumption and highly-scalability simultaneously.