A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation.A data allocation strategy based on capacity and workload is introduced to achieve local load balance,and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster.Moreover,data integrity is protected by using session reassemble and session partitioning.The simulation results show that the new model enjoys favorable advantages such as good load balance,higher detection rate and detection efficiency.

Utilization-Aware Data Variable Allocation on NVM- Based SPM in Real-Time Embedded Systems

With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the lifetime.The lifetime can be formulated indirectly as the write counts on each SPM address.Since the differences between global variables and stack variables,our optimization model has three constraints.The constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack variables.To satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM addresses.Finally,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our algorithm.The experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.

Survivals after liver transplantation for hepatocellular carcinoma:Granular data for a better allocation process?

To the Editor:A large international study has been recently published focusing on the combination of morphological aspects and alpha-fetoprotein(AFP)as predictors of survival in patients with hepatocellular cancer(HCC)treated with liver transplantation(LT)[1].As a matter of fact,morphology and biology represent the two sides of the same

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine（VM） allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant＇s VMs may considerably impact the tenant＇s Quality of Service（Qo S）. In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs＇ network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem（LP-MKP）. The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants＇ overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.