#IstioCon How HP set up secure and wise platform with Istio John Zheng/ john.zheng@hp.com #IstioCon Agenda ➢ HP Horizon platform design with Istio ➢ Secure Platform ➢ Wise Platform ➢ Excellent Excellent Observability ➢ Q & A #IstioCon HP Horizon Platform design with Istio #IstioCon HP Horizon Platform HP has lots of projects, deployed on cloud. They have common features, also have project Replicated control planes Some standalone cluster without Istio can access core cluster also, as tenant. HP Horizon Platform Connect With Istio #IstioCon Secure Platform • JWT Verify • Mutual TLS • Authorization

q : Prop) : p ∧ q → q ∧ p := assume hpq : p ∧ q, have hp : p, from and.left hpq, have hq : q, from and.right hpq, show q ∧ p, from and.intro hq hp If you are reading the book online, you will see a button theorem command introduces a new theorem: constants p q : Prop theorem t1 : p → q → p := λ hp : p, λ hq : q, hp This looks exactly like the definition of the constant function in the last chapter, the will show you the proof of a theorem. theorem t1 : p → q → p := λ hp : p, λ hq : q, hp #print t1 Notice that the lambda abstractions hp : p and hq : q can be viewed as temporary assumptions in the proof

readAndAccess(Func userFn) { // Called frequently hazard_pointer hp = make_hazard_pointer(); // Construct hazard pointer. T* ptr = hp.protect(src_); // Get pointer to a protected object hazard_pointer hp[3]; /* Three hazard pointers are made nonempty separately. */ hp[0] = make_hazard_pointer(); hp[1] = make_hazard_pointer(); hp[2] = make_hazard_pointer(); assert(!hp[0].empty()); empty()); assert(!hp[1].empty()); assert(!hp[2].empty()); /* src is atomic */ T* ptr = hp[0].protect(src); /* etc */ } /* Three nonempty hazard pointers are destroyed separately. */ Batch

[$i]Field[$index]) . 1 . 4 W ha t i s Y i i Yi i i s a h i gh - p e r f or m an c e c om p on e n t - b as e d P HP f r am e w or k f or d e v e l op i n g l ar ge - s c al e W e b ap p l i c at i on s . I t e n ab Yi i - p ow e r e d W e b ap p l i c at i on , y ou n e e d a W e b s e r v e r s u p p or t i n g P HP 5. 1. 0 or h i gh e r . F or d e v e l op e r s w h o w an t t o u s e Yi i , u n d e r s t an d i t h O t he r F r a m e w o r k s ? Li k e m os t P HP f r am e w or k s , Yi i i s an M VC f r am e w or k . Yi i e x c e l s ov e r ot h e r P HP f r am e w or k s i n t h at i t i s e ffi c i e n t

i i 3 1 . 3 W ha t i s Y i i Yi i i s a h i gh - p e r f or m an c e c om p on e n t - b as e d P HP f r am e w or k f or d e v e l op i n g l ar ge - s c al e W e b ap p l i c at i on s . I t e n ab Yi i - p ow e r e d W e b ap p l i c at i on , y ou n e e d a W e b s e r v e r s u p p or t i n g P HP 5. 1. 0 or h i gh e r . F or d e v e l op e r s w h o w an t t o u s e Yi i , u n d e r s t an d i t h O t he r F r a m e w o r k s ? Li k e m os t P HP f r am e w or k s , Yi i i s an M VC f r am e w or k . Yi i e x c e l s ov e r ot h e r P HP f r am e w or k s i n t h at i t i s e ffi c i e n t

chapter 3. We have an additional function build_hp_model() here which takes a hp parameter that refers to a keras_tuner. HyperParameters() object. The hp parameter is used to create hyperparameters which hyperparameters: learning_rate in range [.0001, .01] and dropout_rate in range [.1, .8]. The build_hp_model() is called by the tuner to create a model for each trial with the chosen values for the learning_rate 0002 NUM_CLASSES = 102 def build_hp_model(hp): if hp: learning_rate = hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="log" ) dropout_rate = hp.Float( "dropout_rate", min_value=

Components\IPT;C :\Program Files\Intel\Intel(R) Management Engine Components\IPT;c:\Program Files (x86)\HP\HP Performance Advisor;C:\Program Files\Git\cmd;C :\Program Files\Java\jdk1.8.0_131\bin;C:\apache­maven­3 Components\IPT;C:\Program Files\Intel \Intel(R) Management Engine Components\IPT;c:\Program Files (x86)\HP\HP Performance Advisor;C:\Program Files\Git\cmd;C:\Program Files\Java\jdk1.8.0_131\bin;C:\apache­maven­3 erShell\v1.0\Modules PTSMINSTALLPATH c:\Program Files\HP\HP ProtectTools Security Manager\Bin\ PTSMINSTALLPATH_X86 C:\Program Files (x86)\HP\HP ProtectTools Security Manager\Bin\ PUBLIC C:\Users\Public

(including PostScript level 2) • HP DeskJet 500, 500C, 520, 540C, 600C, 660C, 68xC, 69xC, 850C, 855C, 855Cse, 855Cxi, 870Cse, 870Cxi, 1200C and 1600C printers • HP LaserJet II, III, IIIp, IIIsi, 4, 4L that is often used to convert PostScript to another print- ing language, such as PCL, which is used by HP printers. Since PostScript is the most common printing output for UNIX programs, GhostScript is often with the printer system to convert the output on the fly from PostScript to PCL, which is fed to the HP printer. The G imp Us e r’s Man u al 9 7 C h a p ter 6 However, if you have GhostScript installed

that often work: If the goal’s target is an implication P → Q , invoke intro hP to move P into your hypotheses: . . ., hP : P ⊢ Q . If the goal’s target is a universal quantification ∀x : σ, Q , invoke ¬ P is syntactically equal to ⊢ P → false up to compu- tation, so you can invoke intro hP to produce the subgoal hP : P ⊢ false. Expanding negation’s definition by invoking rw not_def (described in Sec- , show p t, from begin apply hall t, refl end) (assume hp : p t, fix x , assume heq : x = t, show p x , from begin rw heq, ex act hp end) The proof may look intimidating, but it was not hard

pointer A SRC A hazard pointer is a single-writer multi-reader pointer. set HP to A if SRC == A clear HP 2 3 6 if HP != A HP Safe to delete A A 7 8 SAFE ACCESS If a hazard pointer points to an object