Cloud-Assisted VR

Introduction Most of the virtual reality applications are designed for single user. Require high-end desktop or mobile phone in order to provide good QoS. Recently, social VR is emerging as a popular social media. Support multiple users. Cloud servers are required to coordinate the states and interactions between users.

Social VR Users interact with each others with their avatars in a virtual “chat room”. With voice and (mostly) the movements of head and hands. Examples: vTime: https://www.youtube.com/watch?v=cUI6C_0T5_U Facebook social VR: https://www.youtube.com/watch?v=W71o4RbckNA

Social VR in the Cloud There are multiple VR chat rooms in a data center. Each VR chat room is independent and should be isolated with the others. VR chat rooms may have different resource requirements according to the number of connected users and virtual objects created. We apply container technique to host VR chat rooms in the data center.

Container Technique A light-weight operating-system-level virtualization method. Each container is an isolated user-space instance. Compared to virtual machine Lower booting overhead. More flexible in resource allocation. VM：根據使用者設定的規格分配資源，沒用到的資源無法讓別人使用。(獨佔) Container：使用者設定的規格為資源使用“上限”，根據實際使用資源量來分配。還沒用到的資源可以分給別人使用。

Scenario Given containers hosting VR chat rooms, we need to dynamically deploy these containers according to their resource requirements, in order to maintain the performance / QoS. The resource required by a container depends on the number of connected users and virtual objects created in the VR chat rooms.

Scenario(Cont.) 1. “Create” Request from User Application manager VR Chat room Application manager 1. “Create” Request from User Resource manager 2. Prepare a container hosting VR chat room 3. Deploy to server and start the container Deploy strategy

Objective Guarantee the performance / QoS while maximizing the system resource utilization. Use penalty to estimate the performance. Minimize the total cost. Allocating insufficient resources to a VR chat room leads to performance degradation, which generates penalty. Low resource utilization increases the operating cost of a data center.

Container Deployment Strategies We propose five strategies for deploying a container to a server. A. Best-Fit B. Next-Fit (Round-Robin + First-Fit) C. Worst-Fit D. Spread E. Age-Based 將 container 指派到可利用資源與目前需求最接近的 server 上 將 container 指派到 server list 中下一個可利用資源大於目前需求的 server 上 將 container 指派到可利用資源大於目前需求，且差距最大的 server 上 將 container 指派到已運行 container 數量最少且可利用資源足夠的 server 上。若平手則隨意挑 將 container 指派到資源足夠的 server中，已運行 container 開啟時間最久的 server 上

Resource Allocation for Containers Since the resource requirement of a container varies through time, we also consider different resource requirements during the deployment of a container. I. According to container specification II. According to actual usage III. According to actual usage + reservation 依照 container 規格要求資源。 Server 會依照 container 規格，保留資源給該 container，即使目前實際需求還不到這麼多。 依照目前實際需求要求資源 要求略大於目前實際需求的資源

Simulations We conduct some simulation to compare the performance and resource utilization of the above mentioned strategies. Setting: Server specification: (Core, Memory, Network Bandwidth) : (16, 10GB, 1GBits/s) Container specification: (Core, Memory, Network Bandwidth) : (4, 1GB, 256MBit/s)

Workloads We generate 5 sets of workload traces. Each trace simulates the changes in the number of users and virtual objects of a VR chat room. A newly created container will choose 1 of these 5 traces as its workload trace before being deployed to server.

Simulation Results

Summary We apply social VR as our targeting scenario in cloud-assisted VR. Each container hosts a virtual chat room. The resource requirement of each container is related to the number of users and virtual objects, which varies through time. We propose five deployment strategies in order to optimizing the performance of applications and resource utilization of data center.

Summary(Cont.) The simulation results show that … We will apply different social VR applications to enrich the workloads in the data center in the next year. Also, we will consider the heterogeneity of servers.