More Like this

1. OpenStack Network Acceleration Scheme for Datacenter Intelligent Applications

   https://doi.org/10.1109/CLOUD.2018.00146  

   Phan, Linh ; Liu, Kaikai ( July 2018 , 2018 IEEE 11th International Conference on Cloud Computing (CLOUD))

   Cloud virtualization and multi-tenant networking provide Infrastructure as a Service (IaaS) providers a new and innovative way to offer on-demand services to their customers, such as easy provisioning of new applications and better resource efficiency and scalability. However, existing data-intensive intelligent applications require more powerful processors, higher bandwidth and lower-latency networking service. In order to boost the performance of computing and networking services, as well as reduce the overhead of software virtualization, we propose a new data center network design based on OpenStack. Specifically, we map the OpenStack networking services to the hardware switch and utilize hardware-accelerated L2 switch and L3 routing to solve the software limitations, as well as achieve software-like scalability and flexibility. We design our prototype system via the Arista Software-Defined-Networking (SDN) switch and provide an automatic script which abstracts the service layer that decouples OpenStack from the physical network infrastructure, thereby providing vendor-independence. We have evaluated the performance improvement in terms of bandwidth, delay, and system resource utilization using various tools and under various Quality-of-Service (QoS) constraints. Our solution demonstrates improved cloud scaling and network efficiency via only one touch point to control all vendors' devices in the data center.
2. Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency

   https://doi.org/10.1287/opre.2021.2138  

   Perez-Salazar, Sebastian ; Menache, Ishai ; Singh, Mohit ; Toriello, Alejandro ( October 2021 , Operations Research)

   Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, among distinct users with different demand patterns as well as different quality of service requirements. To ensure these service requirements, cloud offerings often come with a service level agreement (SLA) between the provider and the users. A SLA specifies the amount of a resource a user is entitled to utilize. In many cloud settings, providers would like to operate resources at high utilization while simultaneously respecting individual SLAs. There is typically a trade-off between these two objectives; for example, utilization can be increased by shifting away resources from idle users to “scavenger” workload, but with the risk of the former then becoming active again. We study this fundamental tradeoff by formulating a resource allocation model that captures basic properties of cloud computing systems, including SLAs, highly limited feedback about the state of the system, and variable and unpredictable input sequences. Our main result is a simple and practical algorithm that achieves near-optimal performance on the above two objectives. First, we guarantee nearly optimal utilization of the resource even if compared with themore »omniscient offline dynamic optimum. Second, we simultaneously satisfy all individual SLAs up to a small error. The main algorithmic tool is a multiplicative weight update algorithm and a primal-dual argument to obtain its guarantees. We also provide numerical validation on real data to demonstrate the performance of our algorithm in practical applications.« less
3. Application-specific, Dynamic Reservation of 5G Compute and Network Resources by Using Reinforcement Learning

   Gholami, Anousheh ; Rao, Kunal ; Hsiung, Wang-Pin ; Po, Oliver ; Sankaradas, Murugan ; Baras, John S. ; Chakradhar, Srimat ( August 2022 , Proceedings ACM SIGCOMM 2022 Workshop on Network-Application Integration (NAI'22))

   5G services and applications explicitly reserve compute and network resources in today’s complex and dynamic infrastructure of multi-tiered computing and cellular networking to ensure application-specific service quality metrics, and the infrastructure providers charge the 5G services for the resources reserved. A static, one-time reservation of resources at service deployment typically results in extended periods of under-utilization of reserved resources during the lifetime of the service operation. This is due to a plethora of reasons like changes in content from the IoT sensors (for example, change in number of people in the field of view of a camera) or a change in the environmental conditions around the IoT sensors (for example, time of the day, rain or fog can affect data acquisition by sensors). Under-utilization of a specific resource like compute can also be due to temporary inadequate availability of another resource like the network bandwidth in a dynamic 5G infrastructure. We propose a novel Reinforcement Learning-based online method to dynamically adjust an application’s compute and network resource reservations to minimize under-utilization of requested resources, while ensuring acceptable service quality metrics. We observe that a complex application-specific coupling exists between the compute and network usage of an application. Our proposed methodmore »learns this coupling during the operation of the service, and dynamically modulates the compute and network resource requests to minimize under-utilization of reserved resources. Through experimental evaluation using real-world video analytics application, we show that our technique is able to capture complex compute-network coupling relationship in an online manner i.e. while the application is running, and dynamically adapts and saves up to 65% compute and 93% network resources on average (over multiple runs), without significantly impacting application accuracy.« less
4. FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute

   Wang, Ao ; Chang, Shuai ; Tian, Huangshi ; Wang, Hongqi ; Yang, Haoran ; Li, Huiba ; Du, Rui ; Cheng, Yue. ( July 2021 , 2021 USENIX Annual Technical Conference (USENIX ATC 21))

   Serverless computing, or Function-as-a-Service (FaaS), enables a new way of building and scaling applications by allowing users to deploy fine-grained functions while providing fully-managed resource provisioning and auto-scaling. Custom FaaS container support is gaining traction as it enables better control over OSes, versioning, and tooling for modernizing FaaS applications. However, providing rapid container provisioning introduces non-trivial challenges for FaaS providers, since container provisioning is costly, and real-world FaaS workloads exhibit highly dynamic patterns. In this paper, we design FaaSNet, a highly-scalable middleware system for accelerating FaaS container provisioning. FaaSNet is driven by the workload and infrastructure requirements of the FaaS platform at one of the world's largest cloud providers, Alibaba Cloud Function Compute. FaaSNet enables scalable container provisioning via a lightweight, adaptive function tree (FT) structure. FaaSNet uses an I/O efficient, on-demand fetching mechanism to further reduce provisioning costs at scale. We implement and integrate FaaSNet in Alibaba Cloud Function Compute. Evaluation results show that FaaSNet: (1) finishes provisioning 2,500 function containers on 1,000 virtual machines in 8.3 seconds, (2) scales 13.4× and 16.3× faster than Alibaba Cloud's current FaaS platform and a state-of-the-art P2P container registry (Kraken), respectively, and (3) sustains a bursty workload using 75.2% less time thanmore »an optimized baseline.« less
5. Stealthy-Shutdown: Practical Remote Power Attacks in Multi - Tenant FPGAs

   https://doi.org/10.1109/ICCD50377.2020.00097  

   Luo, Yukui ; Gongye, Cheng ; Ren, Shaolei ; Fei, Yunsi ; Xu, Xiaolin ( October 2020 , IEEE International Conference on Computer Design: VLSI in Computers and Processors)

   With the deployment of artificial intelligent (AI) algorithms in a large variety of applications, there creates an increasing need for high-performance computing capabilities. As a result, different hardware platforms have been utilized for acceleration purposes. Among these hardware-based accelerators, the field-programmable gate arrays (FPGAs) have gained a lot of attention due to their re-programmable characteristics, which provide customized control logic and computing operators. For example, FPGAs have recently been adopted for on-demand cloud services by the leading cloud providers like Amazon and Microsoft, providing acceleration for various compute-intensive tasks. While the co-residency of multiple tenants on a cloud FPGA chip increases the efficiency of resource utilization, it also creates unique attack surfaces that are under-explored. In this paper, we exploit the vulnerability associated with the shared power distribution network on cloud FPGAs. We present a stealthy power attack that can be remotely launched by a malicious tenant, shutting down the entire chip and resulting in denial-of-service for other co-located benign tenants. Specifically, we propose stealthy-shutdown: a well-timed power attack that can be implemented in two steps: (1) an attacker monitors the realtime FPGA power-consumption detected by ring-oscillator-based voltage sensors, and (2) when capturing high power-consuming moments, i.e., the power consumptionmore »by other tenants is above a certain threshold, she/he injects a well-timed power load to shut down the FPGA system. Note that in the proposed attack strategy, the power load injected by the attacker only accounts for a small portion of the overall power consumption; therefore, such attack strategy remains stealthy to the cloud FPGA operator. We successfully implement and validate the proposed attack on three FPGA evaluation kits with running real-world applications. The proposed attack results in a stealthy-shutdown, demonstrating severe security concerns of co-tenancy on cloud FPGAs. We also offer two countermeasures that can mitigate such power attacks.« less